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   "cell_type": "code",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xlws/repos/FCNT_bak/inputproducer.py:85: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  roi = convas[cy-half:cy+half, cx-half:cx+half, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old English sheepdog, bobtail 0.654105\n",
      "miniature poodle 0.0409054\n",
      "Tibetan terrier, chrysanthemum dog 0.0321338\n",
      "standard poodle 0.0294567\n",
      "Sealyham terrier, Sealyham 0.0269377\n",
      "0.0013186425201 max of mask\n",
      "(224, 224, 3)\n",
      "250 max convas\n",
      "[]\n",
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      "0.0013186425201 max of mask\n",
      "(224, 224, 3)\n",
      "228 max convas\n",
      "[]\n",
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    {
     "data": {
      "text/plain": [
       "'\\n# Gen anotated mask for target arear\\nsgt_M = inputProducer.gen_mask(lselCNN.pre_M_size)\\nggt_M = inputProducer.gen_mask(gselCNN.pre_M_size)\\n\\n## Train selCNN networks with first frame roi\\n# reshape gt_M for compatabilities\\nsgt_M = sgt_M[np.newaxis,:,:,np.newaxis]\\nggt_M = ggt_M[np.newaxis,:,:,np.newaxis]\\n\\nfeed_dict = {vgg.imgs: [roi_t0], \\n\\t\\t\\tlselCNN.gt_M: sgt_M,\\n\\t\\t\\tgselCNN.gt_M: ggt_M} # corrpus the other nets?\\n\\ntrain_selCNN(sess, lselCNN, feed_dict)\\ntrain_selCNN(sess, gselCNN, feed_dict)\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Main script for FCNT tracker. \n",
    "\"\"\"\n",
    "#%%\n",
    "# Import custom class and functions\n",
    "from inputproducer import InputProducer\n",
    "from tracker import TrackerVanilla\n",
    "from vgg16 import Vgg16\n",
    "from selcnn import SelCNN\n",
    "from sgnet import GNet, SNet\n",
    "from utils import img_with_bbox, IOU_eval\n",
    "\n",
    "import numpy as np \n",
    "import tensorflow as tf\n",
    "\n",
    "import os\n",
    "\n",
    "#%%\n",
    "tf.app.flags.DEFINE_integer('iter_step_sel', 200,\n",
    "                          \"\"\"Number of steps for trainning\"\"\"\n",
    "                          \"\"\"selCNN networks.\"\"\")\n",
    "tf.app.flags.DEFINE_integer('iter_step_sg', 50,\n",
    "                          \"\"\"Number of steps for trainning\"\"\"\n",
    "                          \"\"\"SGnet works\"\"\")\n",
    "tf.app.flags.DEFINE_integer('num_sel', 384,\n",
    "                          \"\"\"Number of feature maps selected.\"\"\")\n",
    "tf.app.flags.DEFINE_integer('iter_max', 200,\n",
    "\t\t\t\t\t\t\t\"\"\"Max iter times through imgs\"\"\")\n",
    "\n",
    "FLAGS = tf.app.flags.FLAGS\n",
    "\n",
    "## Define varies path\n",
    "DATA_ROOT = 'data/Dog1'\n",
    "IMG_PATH = os.path.join(DATA_ROOT, 'img')\n",
    "GT_PATH = os.path.join(DATA_ROOT, 'groundtruth_rect.txt')\n",
    "VGG_WEIGHTS_PATH = 'vgg16_weights.npz'\n",
    "#%%\n",
    "def train_selCNN(sess, selCNN, feed_dict):\n",
    "\t# Initialize variables\n",
    "\tglobal_step = tf.Variable(0, trainable=False)\n",
    "\tselCNN_vars = selCNN.variables \n",
    "\tinit_vars_op = tf.initialize_variables(selCNN_vars + [global_step], name='init_selCNN')\n",
    "\tsess.run(init_vars_op)\n",
    "\n",
    "\t# Retrive trainning op\n",
    "\ttrain_op, losses, lr, optimizer = selCNN.train_op(global_step)\n",
    "\tprint(sess.run(tf.report_uninitialized_variables()))\n",
    "\t# Train for iter_step_sel times\n",
    "\t# Inspects loss curve and pre_M visually\n",
    "\tfor step in range(FLAGS.iter_step_sel):\n",
    "\t\t_, total_loss, lr_ = sess.run([train_op, losses, lr], feed_dict=feed_dict)\n",
    "\t\tprint(total_loss)\n",
    "\n",
    "\n",
    "def train_sgNet(sess, gnet, snet, sgt_M, ggt_M, feed_dict):\n",
    "\t\"\"\"\n",
    "\tTrain sgnet by minimize the loss\n",
    "\tLoss = Lg + Ls\n",
    "\twhere Li = |pre_Mi - gt_M|**2 + Weights_decay_term_i\n",
    "\n",
    "\t\"\"\"\n",
    "\t# Initialize sgNet variables\n",
    "\tsgNet_vars = gnet.variables + snet.variables\n",
    "\tinit_SGNet_vars_op = tf.initialize_variables(sgNet_vars, name='init_sgNet')\n",
    "\tsess.run(init_SGNet_vars_op)\n",
    "\n",
    "\t# Define composite loss\n",
    "\ttotal_losses = snet.loss(sgt_M) + gnet.loss(ggt_M)\n",
    "\n",
    "\t# Define trainning op\n",
    "\toptimizer = tf.train.GradientDescentOptimizer(1e-6)\n",
    "\ttrain_op = optimizer.minimize(total_losses, var_list= sgNet_vars)\n",
    "\n",
    "\tfor step in range(FLAGS.iter_step_sg):\n",
    "\t\tloss, _ = sess.run([total_losses, train_op], feed_dict = feed_dict)\n",
    "\t\tprint(loss)\n",
    "\n",
    "\n",
    "\n",
    "def gen_mask_phi(img_sz, loc):\n",
    "\tx,y,w,h = loc\n",
    "\tphi = np.zeros(img_sz)\n",
    "\tphi[y-int(0.5*h): y+int(0.5*h), x-int(0.5*w):x+int(0.5*w)] = 1\n",
    "\treturn phi\n",
    "#%%\n",
    "\n",
    "## Instantiate inputProducer and retrive the first img\n",
    "# with associated ground truth. \n",
    "inputProducer = InputProducer(IMG_PATH, GT_PATH)\n",
    "img, gt, t  = next(inputProducer.gen_img)\n",
    "roi_t0, _, _ = inputProducer.extract_roi(img, gt)\n",
    "\n",
    "# Predicts the first img.\n",
    "sess = tf.Session()\n",
    "sess.run(tf.initialize_all_variables())\n",
    "vgg = Vgg16(VGG_WEIGHTS_PATH, sess)\n",
    "vgg.print_prob(roi_t0, sess)\n",
    "\n",
    "## At t=0. Perform the following:\n",
    "# 1. Train selCNN network for both local and gloabl feature maps\n",
    "# 2. Train G and S networks.\n",
    "assert t == 0\n",
    "\n",
    "## Train selCNN networks with first frame roi\n",
    "# reshape gt_M for compatabilities\n",
    "# Gen anotated mask for target arear\n",
    "\n",
    "lselCNN = SelCNN('sel_local', vgg.conv4_3, (1,28,28,1))\n",
    "sgt_M = inputProducer.gen_mask(lselCNN.pre_M_size)\n",
    "sgt_M = sgt_M[np.newaxis,:,:,np.newaxis]\n",
    "feed_dict = {vgg.imgs: [roi_t0], lselCNN.gt_M: sgt_M}\n",
    "train_selCNN(sess, lselCNN, feed_dict)\n",
    "\n",
    "\n",
    "gselCNN = SelCNN('sel_global', vgg.conv5_3, (1,14,14,1))\n",
    "ggt_M = inputProducer.gen_mask(gselCNN.pre_M_size)\n",
    "ggt_M = ggt_M[np.newaxis,:,:,np.newaxis]\n",
    "feed_dict[gselCNN.gt_M] = ggt_M # corrpus the other nets?\n",
    "train_selCNN(sess, gselCNN, feed_dict)\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "# Gen anotated mask for target arear\n",
    "sgt_M = inputProducer.gen_mask(lselCNN.pre_M_size)\n",
    "ggt_M = inputProducer.gen_mask(gselCNN.pre_M_size)\n",
    "\n",
    "## Train selCNN networks with first frame roi\n",
    "# reshape gt_M for compatabilities\n",
    "sgt_M = sgt_M[np.newaxis,:,:,np.newaxis]\n",
    "ggt_M = ggt_M[np.newaxis,:,:,np.newaxis]\n",
    "\n",
    "feed_dict = {vgg.imgs: [roi_t0], \n",
    "\t\t\tlselCNN.gt_M: sgt_M,\n",
    "\t\t\tgselCNN.gt_M: ggt_M} # corrpus the other nets?\n",
    "\n",
    "train_selCNN(sess, lselCNN, feed_dict)\n",
    "train_selCNN(sess, gselCNN, feed_dict)\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "## At t>0. Perform target localization and distracter detection at every frame,\n",
    "## perform SNget adaptive update every 20 frames, perform SNet discrimtive \n",
    "## update if distracter detection return True.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected maps shape: (1, 28, 28, 384)\n",
      "Selected maps shape: (1, 14, 14, 384)\n",
      "[1, 14, 14, 36] conv1 shape\n",
      "[1, 14, 14, 1] conv shape\n",
      "Shape of the out put heat map for GNet is [1, 14, 14, 1]\n",
      "[1, 28, 28, 36] conv1 shape\n",
      "[1, 28, 28, 1] conv shape\n",
      "Shape of the out put heat map for SNet is [1, 28, 28, 1]\n",
      "3.81369e+06\n",
      "587.055\n",
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      "13.7911\n",
      "13.0625\n",
      "12.4123\n",
      "11.826\n",
      "11.2915\n",
      "10.8008\n",
      "10.3467\n",
      "9.92522\n",
      "9.53296\n",
      "9.16714\n",
      "8.82449\n",
      "8.50235\n",
      "8.19811\n",
      "7.91019\n",
      "7.63758\n",
      "7.3794\n",
      "7.13485\n",
      "6.90313\n",
      "6.68348\n",
      "6.4752\n",
      "6.27765\n",
      "6.09021\n",
      "5.91232\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Perform saliency maps selection \n",
    "s_sel_maps, s_idx = lselCNN.sel_feature_maps(sess, vgg.conv4_3, feed_dict,FLAGS.num_sel)\n",
    "g_sel_maps, g_idx = gselCNN.sel_feature_maps(sess, vgg.conv5_3, feed_dict,FLAGS.num_sel)\n",
    "\n",
    "assert isinstance(s_sel_maps, np.ndarray)\n",
    "assert isinstance(g_sel_maps, np.ndarray)\n",
    "assert len(s_sel_maps.shape) == 4\n",
    "\n",
    "# Instantiate G and S networks.\n",
    "gnet = GNet('GNet', g_sel_maps.shape)\n",
    "snet = SNet('SNet', s_sel_maps.shape)\n",
    "\n",
    "## Train G and S nets by minimizing a composite loss.\n",
    "## with feeding selected saliency maps for each networks.\n",
    "feed_dict = {gnet.input_maps: g_sel_maps, snet.input_maps: s_sel_maps}\n",
    "train_sgNet(sess, gnet, snet, sgt_M, ggt_M, feed_dict)\n",
    "s_sel_maps_t0 = s_sel_maps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from queue import Queue\n",
    "from scipy.misc import imresize\n",
    "from operator import add\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "class Tracker:\n",
    "\t\"\"\"\n",
    "\tGeneric tracking model. A location is represented by an affine transformation (e.g., Xt−1), which warps the\n",
    "\tcoordinate system so that the target lies within the unit square. Particles representing possible target locations Xt, \n",
    "\tat time t are sampled according to P(Xt|Xt−1), which in this case is a diagonal-covariance Gaussian centered at Xt−1.\n",
    "\t\n",
    "\tWhere:\n",
    "\tXt = (xt, yt, θt, st, αt, φt)\n",
    "\tdenote x, y translation, rotation angle, scale, aspect ratio, and skew direction at time t.\n",
    "\n",
    "\tP(Xt|Xt−1) = N (Xt; Xt−1, Ψ)\n",
    "\twhere Ψ is a diagonal covariance matrix whose elements are the corresponding variances of affine parameters, assumes the variance of each affine parameter does not change over time\n",
    "\n",
    "\tSee 3.3.1 Dynamic model in http://www.cs.toronto.edu/~dross/ivt/RossLimLinYang_ijcv.pdf for reference\n",
    "\n",
    "\tParticle filter calss\"\"\"\n",
    "\tdef __init__(self, init_location,):\n",
    "\t\tself.conf_q = Queue(maxsize=2)\n",
    "\t\tself.pre_M_q = Queue(maxsize=2)\n",
    "\t\tself.last_two_loc_q = Queue(maxsize=2)\n",
    "\n",
    "\t\tself.location = init_location\n",
    "\t\tself.params = self._init_params(init_location)\n",
    "\n",
    "\n",
    "\n",
    "\tdef _init_params(self, init_location):\n",
    "\t\t\"\"\"Initialize tracker's parameters\"\"\"\n",
    "\n",
    "\t\tparams = {'p_sz': 64, 'p_num': 700, 'min_conf': 0.2, \n",
    "\t\t\t\t'mv_thr': 0.1, 'up_thr': 0.35, 'roi_scale': 2}\n",
    "\t\tdiag_s = np.ceil((init_location[2]**2 + init_location[3]**2)**0.5/7)\n",
    "\t\tparams['aff_sig'] = [diag_s, diag_s, 0.004, 0.0, 0.0, 0]\n",
    "\t\tparams['ratio'] = init_location[2] / params['p_sz']\n",
    "\t\t\n",
    "\t\treturn params\n",
    "\n",
    "\tdef _qs_full(self):\n",
    "\t\tif self.conf_q.full() and self.pre_M_q.full():\n",
    "\t\t\treturn True\n",
    "\t\telse:\n",
    "\t\t\treturn False\n",
    "\n",
    "\tdef gen_best_M(self):\n",
    "\t\t\"\"\"Returns the best pre_M in records.\"\"\"\n",
    "\t\tassert self._qs_full()\n",
    "\n",
    "\t\tpre_Ms = [self.pre_M_q.get() for _ in range(2)]\n",
    "\t\tconfs = [self.conf_q.get() for _ in range(2)]\n",
    "\t\tidx = np.argmax(confs)\n",
    "\t\treturn pre_Ms[idx]\n",
    "\n",
    "\n",
    "\tdef draw_particles(self):\n",
    "\t\t\"\"\"\n",
    "\t\tGenerates particles according to \n",
    "\t\tP(Xt|Xt−1) = N (Xt; Xt−1, Ψ)\n",
    "\n",
    "\t\tArgs:\n",
    "\t\t\taff_params: affine parameters, see class doc string for \n",
    "\t\t\t\tspecific element definition.\n",
    "\t\t\t\t[cx, cy, w/p_sz, 0, h/w, 0] for 6 degrees of freendom\n",
    "\t\t\t\t[tlx, tly, w, h] for 4 degrees of freedom.\n",
    "\t\t\t\t.\n",
    "\t\tReturns:\n",
    "\t\t\taff_params_M : self.p_num*dof size matrix,\n",
    "\t\t\t\twhere rows are updated randomly drawed affine \n",
    "\t\t\t\tparams, columns repersent each particles. \n",
    "\t\t\"\"\"\n",
    "\t\tpass\n",
    "\n",
    "\n",
    "\n",
    "\tdef predict_location(self, pre_M, gt_last, resize_factor, t):\n",
    "\t\t\"\"\"\n",
    "\t\tPredict location for each particle. It is calculated by\n",
    "\t\t1. compute the confidence of the i-th candidate, which is \n",
    "\t\t\tthe summation of all the heatmap values within the candidate region.\n",
    "\t\t2. the candidate with the highest confidence value is predicted as target.\n",
    "\n",
    "\t\tArgs:\n",
    "\t\t\timg_siz: tuple(image height, image width)\n",
    "\t\t\tpre_M: predicted heat map\n",
    "\t\t\tt: index of current frame\n",
    "\t\t\"\"\"\n",
    "\t\tpass\n",
    "\n",
    "\n",
    "\tdef get_most_conf_M(self):\n",
    "\t\t\"\"\"Returns the most confidence heat maps.\"\"\"\n",
    "\n",
    "\t\t# Pull self.conf_records all out, and retrive \n",
    "\t\t# the most confident heat map. \n",
    "\n",
    "\t\treturn updated_gt_M\n",
    "\n",
    "\n",
    "\tdef linear_prediction(self):\n",
    "\t\t\"\"\"\n",
    "\t\tPredicts current location linnearly according\n",
    "\t\tto las two frames location. This may boost the \n",
    "\t\trobustnesss of obejct blocking.\n",
    "\t\t\"\"\"\n",
    "\t\tpass\n",
    "\n",
    "\tdef distracted(self):\n",
    "\t\t\"\"\"Distracter detection.\"\"\"\n",
    "\n",
    "\t\t# up-sampling pre_M\n",
    "\n",
    "\t\t# Compute confidence according to \n",
    "\t\t# S = with_in / with_out\n",
    "\t\tconf_within = self.compute_conf(self.pre_M_resized, self.best_p_i_loc)\n",
    "\t\tconf_all = np.sum(self.pre_M_resized)\n",
    "\t\tdistracter_score = conf_within / conf_all\n",
    "\t\tprint('The probability of been distracted is %s'%distracter_score)\n",
    "\t\tif distracter_score > self.params['min_conf']:\n",
    "\t\t\treturn True\n",
    "\t\telse:\n",
    "\t\t\treturn False\n",
    "\t\t\t\n",
    "\t@classmethod\n",
    "\tdef compute_conf(self, roi, loc_p):\n",
    "\t\t\"\"\"Helper func for computing confidence\"\"\"\n",
    "\t\tx,y,w,h = loc_p\n",
    "\t\tconf = np.sum(roi[y-int(0.5*h): y+int(0.5*h), \\\n",
    "\t\t\t\t\tx-int(0.5*w):x+int(0.5*w)])\n",
    "\t\treturn conf\n",
    "\n",
    "\t@classmethod\n",
    "\tdef aff2loc(self, las_loc, aff_param):\n",
    "\t\t\"\"\"Convert affine params to location.\"\"\"\n",
    "\t\tassert len(aff_param)==4, 'This method only works for dof 4 aff space.'\n",
    "\t\tcur_loc = [i+j for i,j in zip(las_loc, aff_param)]\n",
    "\t\treturn cur_loc\n",
    "\n",
    "\n",
    "class TrackerVanilla(Tracker):\n",
    "\t\"\"\"Vanilla tracker\n",
    "\n",
    "\t\tThe covariance matrix has only 4 degrees of freedom,\n",
    "\t\tspecified by vertical, horizontal translation of the central\n",
    "\t\tpoint, variance of the width, variance of the w/h ratio.\n",
    "\n",
    "\t\tThe corrresponding actual senarios are object replacment,\n",
    "\t\tobject zoom in/out, object rotaion. Should be sufficient \n",
    "\t\tfor most cases of car tracking.\n",
    "\n",
    "\t\"\"\"\n",
    "\tdef __init__(self, init_location):\n",
    "\t\tsuper(TrackerVanilla, self).__init__(init_location)\n",
    "\t\tself._update_params()\n",
    "\n",
    "\tdef _update_params(self):\n",
    "\t\t\"\"\"Update aff_sig param.\"\"\"\n",
    "\t\tself.params['aff_sig'] = [10, 10, 0.04, 0.04]\n",
    "\n",
    "\n",
    "\tdef draw_particles(self, last_location):\n",
    "\t\t\"\"\"\n",
    "\t\tThe covariance matrix has only 4 degrees of freedom,\n",
    "\t\tspecified by vertical, horizontal translation of the central\n",
    "\t\tpoint, variance of the width, variance of the w/h ratio.\n",
    "\n",
    "\t\tThe corrresponding actual senarios are object replacment,\n",
    "\t\tobject zoom in/out, object rotaion. Should be sufficient \n",
    "\t\tfor most cases of car tracking.\n",
    "\n",
    "\t\tArgs: \n",
    "\t\t\tlast_location: [tlx, tly, w, h]\n",
    "\t\t\"\"\"\n",
    "\t\t# Define degrees of freedom \n",
    "\t\tdof = len(self.params['aff_sig'])\n",
    "\n",
    "\t\t# Construct an p_num*6 size matrix with with each \n",
    "\t\t# column repersents one particle\n",
    "\n",
    "\t\t#aff_params_M = np.kron(np.ones((self.params['p_num'],1)), np.array(aff_params))\n",
    "\n",
    "\t\t# First onstruct a p_num*dof size normal distribution with \n",
    "\t\t# mean 0 and sigma 1\n",
    "\t\trand_norml_M = np.array([np.random.standard_normal(dof) for _ in range(self.params['p_num'])])\n",
    "\n",
    "\t\t# Then construct a affine sigma matrix\n",
    "\t\taff_sig_M = np.kron(np.ones((self.params['p_num'], 1)), self.params['aff_sig'])\n",
    "\n",
    "\t\t# Update particles \n",
    "\t\tself.aff_params_M = rand_norml_M * aff_sig_M\n",
    "\n",
    "\n",
    "\tdef predict_location(self, pre_M, gt_last, resize_factor, t, roi_size):\n",
    "\t\t\"\"\"\n",
    "\t\tPredict location for each particle. It is calculated by\n",
    "\t\t1. compute the confidence of the i-th candidate, which is \n",
    "\t\t\tthe summation of all the heatmap values within the candidate region.\n",
    "\t\t2. the candidate with the highest confidence value is predicted as target.\n",
    "\n",
    "\t\tArgs:\n",
    "\t\t\timg_siz: tuple(image height, image width)\n",
    "\t\t\tpre_M: predicted heat map\n",
    "\t\t\tt: index of current frame\n",
    "\t\t\"\"\"\n",
    "\t\t# transform self.aff_params_M to location_M with each column \n",
    "\t\t# repersent [cx, cy, w, h] in the pre_M heat map\n",
    "\t\tloc_M = np.zeros(self.aff_params_M.shape)\n",
    "\t\ttlx, tly, w, h = gt\n",
    "\t\tcx, cy = roi_size // 2, roi_size // 2\n",
    "\t\tloc_M[:, 0] = cx\n",
    "\t\tloc_M[:, 1] = cy\n",
    "\t\tloc_M[:, 2] = resize_factor * w \n",
    "\t\tloc_M[:, 3] = resize_factor * h\n",
    "\t\tloc_M += self.aff_params_M\n",
    "\t\tloc_M = loc_M.astype(np.int)\n",
    "\n",
    "\t\t# Upsampling pre_M bicubicly to roi_size\n",
    "\t\tpre_M_resized = imresize(pre_M[0,:,:,0], [roi_size, roi_size], interp='bicubic')\n",
    "\n",
    "\t\t# Compute conf for each particle \n",
    "\t\tconf_lsit = []\n",
    "\t\tfor p_i_loc in loc_M:\n",
    "\t\t\tconf_i = self.compute_conf(pre_M_resized, p_i_loc)\n",
    "\t\t\tconf_lsit += [conf_i]\n",
    "\n",
    "\t\t# Get index and conf score of of the most confident one\n",
    "\t\tidx = np.argmax(conf_lsit)\n",
    "\t\tself.cur_best_conf = conf_lsit[idx]\n",
    "\t\tself.conf_q.put(conf_lsit[idx])\n",
    "\n",
    "\t\t# Store values for computing distraction \n",
    "\t\tself.best_p_i_loc = loc_M[idx]\n",
    "\t\tself.pre_M_resized = pre_M_resized\n",
    "\n",
    "\t\t# Get the corresponding aff_param which is then\n",
    "\t\t# used to predicted the cureent best location\n",
    "\t\tbest_aff =  self.aff_params_M[idx]\n",
    "\t\tprint('The affine paramters: [dx,dy,dw,dh] is {0}'.format(best_aff))\n",
    "\t\tself.pre_location = self.aff2loc(gt_last, best_aff)\n",
    "\n",
    "\t\t# Stack into records queue\n",
    "\n",
    "\n",
    "\t\treturn self.pre_location\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tracker = TrackerVanilla(gt)\n",
    "\n",
    "# Iter imgs\n",
    "gt_last = gt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xlws/repos/FCNT_bak/inputproducer.py:85: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  roi = convas[cy-half:cy+half, cx-half:cx+half, :]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "# Gnerates next frame infos\n",
    "img, gt_cur, t  = next(inputProducer.gen_img)\n",
    "print(t)\n",
    "\n",
    "## Crop a rectangle ROI region centered at last target location.\n",
    "roi, _, resize_factor = inputProducer.extract_roi(img, gt_last)\n",
    "\n",
    "## Perform Target localiation predicted by GNet\n",
    "# Get heat map predicted by GNet\n",
    "feed_dict_vgg = {vgg.imgs : [roi]}\n",
    "s_maps, g_maps = sess.run([vgg.conv4_3, vgg.conv5_3], feed_dict=feed_dict_vgg)\n",
    "s_sel_maps = s_maps[...,s_idx] # np.ndarray, shape = [1,28,28,num_sel]?\n",
    "g_sel_maps = g_maps[...,g_idx]\n",
    "\n",
    "feed_dict_g = { gnet.input_maps: g_sel_maps}\n",
    "pre_M = sess.run(gnet.pre_M, feed_dict=feed_dict_g)\n",
    "tracker.pre_M_q.put(pre_M)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The affine paramters: [dx,dy,dw,dh] is [ -3.01449854e+01   1.97820074e+00  -3.09942053e-02   1.26708898e-02]\n",
      "At time 2, the most confident value is 8495\n"
     ]
    }
   ],
   "source": [
    "# Localize target with monte carlo sampling.\n",
    "tracker.draw_particles(gt_last)\n",
    "pre_loc = tracker.predict_location(pre_M, gt_last, resize_factor, t, 224)\n",
    "print('At time {0}, the most confident value is {1}'.format(t, tracker.cur_best_conf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[77.332849184344852, 133.4397456451525, 50.95978830000665, 36.028893395073922]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Set predicted location to be the next frame's ground truth\n",
    "gt_last = pre_loc\n",
    "\n",
    "# Draw bbox on image. And print associated IoU score.\n",
    "#img_with_bbox(img, pre_loc, gt_cur)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### adpative debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "assert t == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 14, 14, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_M.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "best_M_resized = imresize(best_M[0,:,:,0], [28, 28], interp='bicubic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "loss = snet.loss(best_M_resized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_op = SNet.optimizer.minimize(loss, var_list=snet.variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feed_dict_s = {snet.input_maps: s_sel_maps}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNet adaptive finetune\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n",
      "loss:  None\n"
     ]
    }
   ],
   "source": [
    "print('SNet adaptive finetune')\n",
    "for step in range(20):\n",
    "    loss_, _ = sess.run([train_op, loss], feed_dict = feed_dict_s)\n",
    "    print('loss: ',loss_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### detractor debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of been distracted is 0.0292498338665\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracker.distracted()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### descrimtive finetune "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xlws/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:83: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    }
   ],
   "source": [
    "phi = gen_mask_phi(roi.shape, pre_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv4_3_t0, sgt_M, conv4_3_t, pre_M, phi = s_sel_maps_t0, sgt_M, s_sel_maps, pre_M_g, phi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pre_M_g = sess.run(gnet.pre_M, feed_dict=feed_dict_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'self' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-34-51f6975bd0a6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mLoss_t0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce_mean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msquared_difference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msgt_M\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpre_M\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mfeed_dict_t0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_maps\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mconv4_3_t0\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mtrain_op_t0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSNet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLoss_t0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_list\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'self' is not defined"
     ]
    }
   ],
   "source": [
    "sgt_M = tf.constant(sgt_M, dtype=tf.float32)\n",
    "pre_M = tf.constant(pre_M, dtype=tf.float32)\n",
    "\n",
    "Loss_t0 = tf.reduce_mean(tf.squared_difference(sgt_M, snet.pre_M))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feed_dict_t0 = {snet.input_maps: conv4_3_t0}\n",
    "train_op_t0 = SNet.optimizer.minimize(Loss_t0, var_list=snet.variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pre_M_g = imresize(pre_M_g[0,:,:,0], [28, 28], interp='bicubic')\n",
    "pre_M_g = tf.constant(pre_M_g[np.newaxis,:,:,np.newaxis], dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Const_2059:0' shape=(1, 28, 28, 1) dtype=float32>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_M_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Loss_t =  tf.reduce_sum((1-phi) * tf.reduce_mean(tf.squared_difference(pre_M_g, snet.pre_M)))\n",
    "feed_dict_t = {snet.input_maps: conv4_3_t}\n",
    "train_op_t = SNet.optimizer.minimize(Loss_t0, var_list=snet.variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'SNet/selected_maps:0' shape=(1, 28, 28, 384) dtype=float32>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snet.input_maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv4_3_t = s_sel_maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 28, 28, 384)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv4_3_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feed_dict_t = {snet.input_maps: conv4_3_t}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n",
      "Loss 0: 0.0283147 Loss t: 9.67777e+07\n"
     ]
    }
   ],
   "source": [
    "for step in range(20):\n",
    "    _, loss_0 = sess.run([train_op_t0, Loss_t0], feed_dict_t0)\n",
    "    _, loss_t = sess.run([train_op_t, Loss_t], feed_dict_t)\n",
    "    print('Loss 0:', loss_0, 'Loss t:', loss_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "predict_location() missing 4 required positional arguments: 'gt_last', 'resize_factor', 't', and 'roi_size'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-67-adb28791d0f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# Use location predicted by SNet.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mpre_loc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtracker\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_location\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpre_M_s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: predict_location() missing 4 required positional arguments: 'gt_last', 'resize_factor', 't', and 'roi_size'"
     ]
    }
   ],
   "source": [
    "\n",
    "pre_M_s = sess.run(snet.pre_M, feed_dict=feed_dict)\n",
    "\n",
    "# Use location predicted by SNet.\n",
    "pre_loc = tracker.predict_location(pre_M_s, gt_last, resize_factor, t, 224)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The affine paramters: [dx,dy,dw,dh] is [  6.69195078e+00  -2.17733071e+01  -1.75411874e-02  -1.44165982e-03]\n"
     ]
    }
   ],
   "source": [
    "pre_loc = tracker.predict_location(pre_M_s, gt_last, resize_factor, t, 224)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[84.024799968533131,\n",
       " 111.66643851538579,\n",
       " 50.942247112578784,\n",
       " 36.027451735250722]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def img_with_bbox(img_origin, gt_1):\n",
    "\timg =np.copy(img_origin)\n",
    "\tgt_1 = [int(i) for i in gt_1]\n",
    "\tw, h = gt_1[2:]\n",
    "\ttl_x, tl_y = gt_1[:2]\n",
    "\ttr_x, tr_y = tl_x + w, tl_y \n",
    "\tdl_x, dl_y = tl_x, tl_y + h\n",
    "\tdr_x, dr_y = tl_x + w, tl_y +h\n",
    "\n",
    "\trr1, cc1 = draw.line( tl_y,tl_x, tr_y, tr_x)\n",
    "\trr2, cc2 = draw.line( tl_y,tl_x, dl_y, dl_x)\n",
    "\trr3, cc3 = draw.line( dr_y,dr_x, tr_y, tr_x)\n",
    "\trr4, cc4 = draw.line( dr_y,dr_x, dl_y, dl_x)\n",
    "\timg[rr1, cc1, :] = 1\n",
    "\timg[rr2, cc2, :] = 1\n",
    "\timg[rr3, cc3, :] = 1\n",
    "\timg[rr4, cc4, :] = 1\n",
    "\treturn img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from skimage.io import imshow\n",
    "from skimage import draw\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.imsave('test.png', ib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0002.jpg'"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputProducer.imgs_path_list[t-1].split('/')[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2ca15b1240>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hf4R6vY5SqYTbt2/ja1/7GiqVClqtFr7//e+j2WxiNBphdXXVueQulUrY3t5G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\n6dOnUSqVsLOzg6efftrRKNpfCDUkak6DwQCnTp3C22+/jR/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r6PV6zjacdt6tVgvlchnb\n29tYWFjAF7/4RfzMz/wM6vU6arWaa3MsQxzvuSilZQwP5wWAhYUFDIdDPPvss7h48SKAvY1Ia2tr\n+Pa3v43HH38czWYT+XzeLYL2+31E0d5ZoAT39fV11Ot1lMtlXLv2/7P3pjGWZVe54Hfixp2nuBE3\nhhwqMyszK2u0yxZVZQsXsnk0uLEbP9RIDI0a06hbwFNLD8EPP/oHT2o10N1Sv0d3S0iA6AeFHzwe\nDchGtGxsGUPZuFyzceVUVRmZGZmRMd55jjuc/hH1rVh35z7DjYysyCrnkkJx77nn7LPPPnt/e61v\nr7X2Mp555hlEo1Fsbm4iFotZ6Qa/Sc4mNg3QtDSAca3TBDI/utLLz5t93/yu+7L2Z/e7D90Geb9e\nr4fhcDiWHqFQKOD5559Hq9XC4uIi0uk0ut2upLFtNpuyixDdPOmJ1Gw2ZZcoWky29jI3XdaWlT7P\nNtHyP8vVaXFNbVzjCRdvg+TQqRUvkLPNRn5mlim22f5uUAM2rd+sv5fGHzRQ7svtwhB7RklmMhnU\n63VUq1UUCgUMBgN0u13Mz8+j2Wyi2WwC2J0A6PHy8MMP46mnnsLjjz8uofV6QibNMT09LZ4ozKpI\n7wf6jBOsdnZ28IEPfAArKytYXV1FLpeTMpmnXGtsjUZDNPPRaIRSqYSNjQ3Mzs4ikUiMRRaGFS+g\n9zvfFD/Qtn0/SC3ey4rmJMuQeWAvrwqwNyGsrq5KojRSWrYAK013kfvWucI5OZkBOzY/ck2FmcnW\nNFaZaXK9NHAGArFcbS34yaFr5Pq/Fi9NluLXgbwok/3QBbbyvMqxgbjX716c9H4kzKDZjzVy0PU4\niAlLm7vD4RCLi4vIZDJyjGlp6fbHwZJOp9HpdNBut/HRj34Up06dkrL0LuqkNDqdjqRETafT4gWj\nw/dpHQBALpfDk08+iXK5jK985Svi6cIBTW2ePK3WfLkQ12q1kM/nxywJm0IQRK2Y2qQWk3IJoxCF\nOWbe1/ZdHw+aMMxyqBEDGMuZAkC06ps3byKXy8lE3O/3xzR5/uc7JP01HA4liEtvpKyBFsAY2PO/\nfg7t0qj3DDWTrbEvmc+trSJ6zdAiDJJ7UiMHvBct/TqfF0Ca2v2kAGobTOZn85jtHiaI7+f+NglT\nzkEA9b1wD0ZZErSXlpZw9uzZMT6aroLtdls0t8FggFQqha2tLZw8eRLD4RAbGxuSuEpvnEsvk2g0\nilarhXg8jlQqJVGD2mOGzzw1tbsjzcMPP4xWq4VvfetbwpXrfkhwJ6DU63XZni4ej8NxHEnqZWtb\nL6DjfxuIB4G57Zywx2zj0asOXvfzGgs8R2uv9AOnhw8AoUQajQbS6bRotXz3tOBIbbEs/h6LxcQ9\n0UYp8b/f7j2u645NGiaFpMP99dZzuhyCOK+PRqMC5EFj69CB3NYJvLTaSQHLBHDb/SYV8/qgcg8C\n3N4NDvxe4dmDxPT9TafTkop2MBgIpXHr1i088MADoq2/+uqryGQyY4ON4MnFpkgkgmq1KnnLR6MR\nrl27hlQqhaNHj+LkyZMyUcRiMUlbC0CAJJFI4AMf+ABee+01jEajMbdEJuvSgMGUAvRcYbCSjjjU\nbRukSHj9FgS4QLgYCfN7kDVg++xXvq08vQUa24VuhQBk0ZtZCqvVqkzQzDrJ83O5nFwP7L43+vRT\nW+bEoa0/r7bRExTXNXSQEbAXLGauQ2gxNXH9/z0TEORngtlkUk3Wi3MPW4YfteLlG+pXlt9kZZN7\ngRa5l4SLkPF4fMy3N5PJoN1uS96Uo0eP4siRIwCAF154AaPRCEtLS3AcR3hoUiD0K//iF7+I9fV1\n8UNeXl5GIpHA8ePH8eM//uOywe/Ozo4ABrCXeIkAwkGZTCbFp530gOvu7mDDewAYW2g1F9AoXpq2\nucDGP1sf8wJWU/sMO0n4fQ6aiPzK1ufRSuJxpjigrK+v49VXX0W/30ehUBDQZDxBIpEQSy6TycB1\nXXkPXExMp9PWjSO0mNa1+ZlWAq0tvR0c+6mexM3y2X+i0agAeVisCp/O7r7cl/tyX+7LPSn3tEbO\nc/RnPaMFzfIHtegZVuuwcd9ev9s0/TD3t8lBcOQHuRC53zqEEZ0Dmh4si4uLkqTqxo0byGazOHLk\nCDKZjPh5LywsYDgcCp3S7/dl13V6s9y6dQtf+MIX0Gw2sbS0hEgkgnq9jkQigVKphGeffRapVEoo\nlXa7LbvGMClWNpvF6uqqUCi8F+urvSG4aGf6jOt8K2bb+Vmuftq313HdB/fTj2yadxiNHRh3x/Pj\nyHkO228wGMg7AYCvfOUruHLlClKpFGZmZtBoNGTNhJYaXRgTicRYMA//uOBtLkzyGN0kzfHM9Q5a\nW+ybZvCP2cZaGyfNR/92auR8Xk3TeMmhAzn/m2ail3lpA/JJ7ncQgGWWyXqZ5dv4PhPk9/Mc3+tC\nlzIuHD788MPY3t5Gs9kUU7pQKIzxqCdOnMCNGzdQq9UwGo2EN52enpYozc3NTWxubiKfz4snBN9R\ntVqVcH5SOfRyAIByuYxMJiM5sIfDIeLxuGy0TBNeu7zR9OdEwgEbxvXQT/GZpD9NqtSErVMQNePl\n8WUTvmf6fZOa2tzcBAB89atfxeOPPy7rFOVyGfl8Xvz9eT75Zs2Bk2bhb5FIZMxNlKJD+U36yjwX\nGM/dwsVLHY2sF3D5bMlkcizVrnk/PzlUIPci/gH7QiWPc7bTuYS9yvcTW+PoGVZzmjYQNgeB14q3\nvo9+eeaz2b7regada1o3k0x6fnwqZZKNJbzu4Vd+mGu0C5jWbgqFAgqFAo4dO4bBYIC/+qu/wrVr\n1/DUU08BAB5++GH0+30B/OnpaaytrUl/ovsfg3harRZ2dnaQTCbRbDZRKBSQSCRQqVTEG0ZvB0bt\nfWVlRTZ4LhQKqNVqqNfr+L7v+z6cPXsWFy5cwPb29lgE6mg0QjablfEwPT192wYNplZns0j9NG9b\nWbp9TZc7/jZpvzGtbJan84+Y99H7l3JydBxHPsfjcUl5wKySKysr+M53vgNg1/UznU6j3W5LoBDL\n58bXXAjvdDpiyent9ahxc2MS/VwEfW0lmf2PyqcpLJvvjfeJRCJIJBIA9jxx+JnXmJuS+Mmhe63s\nVybVPLzKAML5g4cBXK9rJ62TF+CZ9dUz/p1KEIh7HZtEvCinoHpNujgdi8UQi8XEXRAACoUCZmdn\ncfr0aXzta1/DcDhEIpEQ33IG98zOzqLZbIpZPjU1hVQqhXQ6jUajIbnONTUD7HmtnD9/Hq+99hpm\nZmbkGU+fPo2HH34YxWJRvGtarRba7TZyuZwsyiYSCRm4YSmUMO9tkrbj/7B9OEjz1jvF6+NayUil\nUgLCruvKoiPze7fbbdm+r1Qq4erVq7hy5Qpu3rwJYBfIdV4dfR+CJj1HaP1MTU1Jdkv9HFTkOJkC\neyH0BFibD7ieDLTonPb8zvLZNpzkbFGvnPDvmFpxHOcPAfxXADZc1/3gO8f+LYD/AcDmO6f9T67r\nfumd334dwC8AGAD4167r/p1f+WEA2Q9wVT1v+zwJWPA6sxOHBXCW4zcAbDSL7RnMe5tavNfvtnvw\n/34G+N2mfMJq5LZ205/17+QZgd3Qbe7gE4/H8dhjj+GZZ57BF77wBfzjP/4jHnzwQTz44IOIRqNY\nW1tDvV7HBz/4QVy5ckW0dMdxUCwWsbS0hEqlgn6/L2lR+/2+0DyxWAyNRkNoFE4MmUwGTz75JB55\n5BHxZU6lUlhdXcXNmzeRTCbxwAMPSN4XggutTS9t2zwWps/p76aVeRCgbRNaLNyiT+cqoUZMgGW0\nLrVfvttUKoWbN2/i4sWLuHz5MtbW1rCzsyOupQsLC5KgTGvRBFEmzOIeqel0Wiwv0mt6TNFjRAcE\n2da1zPejz9GWjgZpnUCNfuwEdWrsLJcgrsP5vSSMRv4fAPzfAJ4zjv8713X/nT7gOM6jAH4SwKMA\njgP4quM4D7kBvcQEMxvfzPP8tLo75fnCaOJBGqI58CbRls37eg3CoMFnTkyT3tvr2EG0b9A9JynL\nBvIcvFygItC2Wi2cO3cOR44cwfnz5/Hmm28ikUjgoYceQiaTQafTQa1Ww6lTp8ZC87vdLo4cOTK2\nGxAjPgeDgRxrNBqoVCpotVooFAqIRqOSmOv06dPiT37q1CkMh0PhQ+fm5jA7OytUjQkQYdrLS1P3\nmgTCgrbfWLMpCbru/NNgRQpBa6katAGMbcJBy+Xtt9/GxYsXcfHiRbRaLaRSqbE0BuVy2bM+BFEu\nOrfbbdH2uWhttrfjOGPgbobqU/tm/zMtDJNi1ZkUdcSmTmus763HN0E86J0FArnrut9wHOek5Sfb\nqP6XAP6T67oDANccx3kLwDMAvu1TfqDGbetQNrAPAlm/Ougy/SRIi/Yr36v+QeUFPZOpsZv3utN2\nMe+zX9kPR24rw2uSB3Y1PwbW9Pt94SGZLGtlZQWFQgHFYhGJRALr6+twXVcWJhn2Pxrt7sHZaDSQ\nyWQQi8VQrVYB7Hq5NBoNlEollMtlALtbyXW7XcRiMRQKBWSzWZw8eRJzc3OIxWKo1+vI5/OSoGtx\ncRG9Xg/5fF4yIVIr8/J48BsPQe0aNDl49eMwk4AXkHtdb+YsYdZKLkA7joNer4dLly7hlVdeQa1W\nk3w3MzMzEp3Jrdj04qXpJMHP3Hii0+nI/qhMY6wXmDUQE2DNMP+gdtTvx1zo1OH7GuwB3EbN+AUQ\nmXInHPn/6DjOfwvgZQC/5rpuDcAxAN9S56y+c8xXgjQ/rw5se7hJwcEGsvu9HvAHK/OzntF5rR+o\nB33XmgG/36nW6/dsd1refuvmR61Qk3NdV/hvYDdjYTwex5tvvjkGoFy81Ca4Do9OpVIYjUay2DY1\nNYXt7W20Wi3xkgF2c7kUi0VJpxuLxVAsFoX/nZ6evi3febFYRC6XE3rBRqd4adtebRsWiLUmaSvf\nzyIMc18eo1cPy9SJoHjvcrmMbDaLRCKBS5cu4eWXX8b6+rosJjMAjJNys9kc2wM1mUyiXq+POT+w\n/lQ+YrGYBOq0Wi3RjF3Xleha1tPGa5sBOmEtdU0jac8Yc5zqd6I/c5IKyreyXyD/XQD/s+u6ruM4\n/wuA/wPAf7+fgibRGv3O8eqUd1In2z396uClLU563yDtyNYBgq4LWye/wXun7RrmflpsYOIH4vy9\n0+ngyJEjY7vyLCwsiH8xAFk8Y4IlekRoj4bRaDTmd8zf6EbIKEEAotETXEi/cDAyj0q/38f8/DzW\n19clNzlBnAtwXrk1gqw0812FBXeb2Pq9rQwb+Og/+mcTHM18IlNTU8jlcnjttdfw8ssvY2VlBZ1O\nB8lkUtYMyFvTdZAgy0mUnii8p06LS61Wa+uNRgPA7iIpwVwrVbp+AKRsvf7iRcmY/+nOqCcCk37R\n7akXOfV9gijafQG567pb6usfAPibdz6vAnhA/Xb8nWNW+dM//VPxEX3sscfwxBNP3DZQ/cRsiDvV\nIE3NeJKJwQbidwLo+tqgxU4/sAurlQcBh9c5k8ik1MqkkyKfdWdnB4899hgeffRRpFIpALvAev36\nddG8SZcw/wmDMtbX14W/dBxH/Lu5IEU/cprmrB+v1+6EnU5nbBCTOigUCtjY2JDyODlwAHPy8Gsf\nv/550BPuJJq5KXpPUz4rUxbwGV566SVJQZvJZGTvU+3fzUmO2nM8Hpf1DzNPOIHPnFQInkyQxvw2\nAKx51W2eJnwWre3blFFtfei8KTaNXo9nPstoNEKtVkOlUoHrugemkTtQnLjjOEuu666/8/W/BvDG\nO5+/COA/Oo7z77FLqZwF8KJXoT/1Uz8lCfi1OWTLMsYH1iaaV+cxgc5LgoBfg6CfFs5zbVRJ0DX6\nWvMlB2nWpsbld98gHj2MmD7ctonFq/ywoBxksmp+FdgbZBwwnU4HlUrlNpcuBt9w2y/uMMQ840x7\ny1wqo9FIAoO4ew/BmtGh8Xh8bMGKGhjBvFAoiOdLNpsVk14HNHFDA4K4mf7W9k619mZra92Wpsao\ntVSTT7YpRvTjpjueTsFKgCFI8/kdx5GF4OvXrwPYXaMol8totVpj2iZTyDrO3obUuq8S3Aj8epMF\n7clBLyFea2IIn1fnUNne3pZ1i0ajcdvkaIK6rhcXp3VKXDOnuV7Y1BMBy9d11XWnBZDJZMTKi8Vi\nWF311IlDuR/+KYBPAJhzHGcFwL8F8IOO43wIwAjANQC/+E7lLjiO858BXADQB/CvXB+ksIEBP2vz\naFIwCCsHUZZNM9ovxaMnA5uY9Z1UW/U7PgnI2gBbl+f3W1jxo7NspikHwrVr1xCJRJDP59Hv90Uj\nr9frKBQK4pFCM5mmu+ZstU8y34fmdgnierMIXk/Ni7SO1qS06xlzjmuNlVyo15jwa8OwbasBihMG\ngLH76nZmgEq325W6EpgIRpzgXNdFr9dDvV5HqVSSxWIAEsnKtiTYm7m5WT+90HenoscVg2v4x1QL\n5N+1z7huGzOBlZ5MtfLJvqSVC61Q6PdoftaTJ2mjoPdOCeO18t9YDv8Hn/N/G8BvB5UL+GvAfhWf\nhHK5U/HTgL0kaMLx+l3TM6Ym5VWnsKLL9bIIgsQPwHV5YY8H1Zf3NMuwATmB9+bNmzhy5IgE6/A6\nLnbpyDr+Tg6VGiUXNbVo7SqVSolmbmrketBSk9LgxXpks1mUSiXhz0kZUMP1skj15zAUh5eFyGNs\nB73htG5jrYVqYCJNUq/XZVGy1+uJ736j0ZC1B/N6grTOCGjWV//tR/yUK239jEYj2SpOL8J6Bf7o\nstgnCPycDDWdosPy9XjT5ZvviN858XPh3U8OPddKUEezXRN0zkECuZcETTR3akX4AblNQw2qm0nb\n6HqGrZ8Jnn7vYr/vwHxuv++67vxP07vX60k+agDI5/O4ceOGXMuBrH10OfC0psj+qYGcAG5u2ss6\n0X+de31qIOeCHVPYagBgOeSGbe0SBrzNtvESPaERvLQlAuylDCYgUYNl6oFKpYKdnR10Oh3htW3v\nQ7ePpk9Ib/GcOwFvXYZuB97TPKa16lqtJgvWTGDFa3SUrxmco8P8NaWiNXGb4uSlkeuy6HcfJp3t\noQK5XqQwB6QfSIfRWv3K8LtmP+KlPXo9ix99YpZpE69nDwJWWxtPMnhs9Q5rRen7+onWRr3akWDJ\n37RLVyqVwvb2tmyKzOg/5u3QWpTWzPRAYnShfkatGenByf+20GzSMPzOuuodhmy+wtrdzPZOvdo7\nTJ8yAUxrhTpLo34+ul82m03U63U0Gg3U63W0Wi2xfMzzSU+ZdJy+J9sjaKyHEa/JjH/aMqBw0qYH\nDKM9c7kcUqnU2GRqUj3aiqPnizlh2/qxl7Wh24fXc6HcdfcyMXrJPauRe53P/7pRJ9UszfL8xKtM\nEwzDXhe2HvvVSrwGu82lLYx1Y54/CbVyN7VyPXD0b51OB+fPn4frunjggQcwMzMDANja2hKfbXo/\naA1UgynpDwACNBrINbWjg0Z4TLc9j1Pz1ot11Nb11m58Jq93ZWpxfm1nnhdk8ZIWIDAxbL7dbmN7\nextra2sol8sSEalBRwMXQc9cwNPvSi9g6mvNuk8C5vo5vNrAxA29SNvtdtHr9cTdlCmKWX+6pdKK\n0GDOempqTbezfq9eIM77sD5cf2G0q58c+ubLutE1J2XrdPplmI11p/TFJBIE7vu51pyU9DHz+fye\n16YhmwPZpuGGqTvP8xp0XvUMq7Hb6upn3WjukQN0YWEBvV4Pzz//PD772c+OnZvP50WD1ItJGtD1\noqY2acl308PERjNpjZMTJ4FuNBqJtwx90IvFouxmxHOHw+FtC2NmG/u12ySiJyLdngBEA3ddF/V6\nHRsbG9jc3ESr1RrTpKnV6negn9vsc1rY7uR/dZtqwLsThYB/2qvF5s5r8vZ6oZz9gAm3qCmbwvP8\nJiavidVmubAenU7n3gdyiqkdevFCJjjcCT0ySef3u4/5m9dzeZ1noxP2W1ebxubVgSYd/EEat+13\nfSxMiL+XpWO2ozlYCMRPP/00Ll68iD/5kz/Br/7qr0rSrEwmI1QATVVqXpqzNf2JTbdCup3pd0fR\nZTFYSNebnHkkEkEqlZK60UXRdfdyY/tZT6Z4nRv0fkxOXHtg0F2wWq1KHhrWk5MZqSqtlZtrD9rX\nW9eLlg8tEra1Ht+TALnfuNF/fp4wvA8To3HSJ53GOpoePtri0pNRkNWur+cfPXuYSIzRyQeRNOuu\niXZH0q5NOhoLsPOQ5kvWjcHvXh16EvD3mkzC/u5nJnsF+oSRSSchc6I0JQzIao3cbG/eh+fZOq+p\nyXppJ/p8rd0RKBgRyf7DAdTv97GxsYFPfepT2NraQrfbxezsLACgVCrBcRzkcjmUSiW02230ej0Z\n3OxbDOLR/sHAXl9l/ZmjQw9w/kb3Q35nAFI0GhVaIp1O4/jx45iensbKyor4UCcSCcmLrt+VTRsP\nAiQuVDLYqNvtyoREmsAsq9Vq4fr165KDhn7tXMgcDodje2Zy4jF9zFmmufuRtjbM9KxBSpqf4qf7\nCeuly4nFYre5lXJs8pjptaJzubDN0um0TMYMbNIBQ8Qv/sZJQNfH9GIZDodC6ZBGIZDr9Ll+cuj5\nyP06p5epPmm5ftrdncqdaLV3Yk0ctExCC3mBsV6gM60A13XHfHXNxSMNkqZoTcpMVaq1IQ6aXq+H\nj33sYzh16hQ2NjYA7AJxLpfDysrKWN04aPhcBCoCMu9LDZSbTdhACNgbpOTTNR2jozWZJIrBMPrZ\nzXbU5U8isVhMANacNOlPTx54NBpha2sLt27dwsbGBlqt1li99RZouh+YGq4NYCk2hcI2/sNYp0Ht\nYcahaKXJxANzAuAETsqHee0ZBcxUuOl0eiwgSi+iswxtpdAn36w7LQBmZuQ7Mxfi/eTQgdwmXi/T\n7AjmubbzzeOTgudBgv57XWwmowaaIK2BHZVlECyBPQA03cR02TTVeZzeJTTlI5HdXXoA4MyZM+JG\nBuymmd3e3h7jQTlgmDObi3zMnc36mJqTpl9Mdzvyp5qOAfaiYqm99Xo9cS9jBKne0kwnm/J6D0F9\nk1ojOXtdb7YZn73RaODGjRvCg2sfax08Y062ug+YlrQNQLVGbk4upkZu9gMvy9JvbLO+WgEwQZzv\nWfv/F4tFWSjv9XoolUqynsEsmIlEAtlsVqxDm0JAyy6bzY7dY2dnR4KlmN1R5/gx/djfM0DuV1Hz\nNz86w9YR9HWTAnMQ8O8H6G2AeNgSlof0a8OgoAUdDchBrH11Xdcdi5Q0gylc1x0zWU0T13V3Fztv\n3rwpaUvpfkjel9w0gYyRfhpkCMI2+oYD1OY2qyMAyaNrgNLU4M7OjmwHd+LECWxubqJer0vq3SCF\nJYxQo6PJT6qDk1UikZB86teuXcOtW7fkfNIDetLTwBnms6aezL5jAjnb0KbEmd9t15rBNaZiYeb0\n9sIU0jBzc3N44IHdtFG0XN5++20MBgPU63UB5enpacmkSX9zrZXT1ZR0HWmudrstEwLzqdOCYhtQ\nCQhDe975HmH35b7cl/tyXw5V7ik/cvM32zFTm9XH/TRxs8x3i5/2u+ed8J+HIXxXtkVlUhw8T19j\nCrVTvdhGrU9r7ZqDpZaTSqWEW+YmAfV6XbxR1tfX0Wg08LM/+7NIpVKicfZ6PaRSKZTLZYnK1L7N\n2jrQfKk+xmch1aC1eJ3djsdI41Aj08f5OR6PY2lpCcCuSU/PGluf1f08TP8lFaLvC4ybAhJkAAAg\nAElEQVRHI3JjjLW1NTQaDbEG6Gev35n+b2rAuq5huW3dn2wWddhxYVp3ur/YtHgbXuhNj03eX7ui\nsk+2Wi2xcLj+UiwWZRtAnT+dC5fcR7bRaKBcLgsNqL1/bF412rvIS+4ZaoWiG5rfTfGjVmzn2cA0\n7GC4U5ANW9fDljB1sg06LZpDJYDo79rzg0BumuIAJGBH5+/gd26nxt1e+Nfr9TAajXD9+nX8xE/8\nBHK53FiEJOvKPNf0HuH9NOXAHYZ4nX5ORohqTwdgbyszftch2mawjOvu7kNJszuTyWB+fh7ZbBY3\nb97ErVu3xIc56B34Ce/tuq54RXARr9fr4e2338b29jYASFZCgg6pF9PrhHXw6gP7GVdBz+QF8ra+\nA+xNwCb9RTE5cmA8qybpk62tvWzdnU7ntkm91WpJeoJcLgdgN11ysVgUkCdHHovFUKlUUC6XhUZj\neVz3YXpds33CyD0D5LYGt2nrQQuYXtrgfvnou6W5H4Z14CdhgcG8Rg8mvSpPbVQv2nCRT4MvfXXb\n7TZ2dnawtbUl5xC8CeRm3m7XdcUzhNrMRz7yEXz2s59FoVDAm2++KRw5NWPy0QRe13UF3MkH66g9\n7XnAexLIWQYASY9rLr4S0AkQemKgRwgntnQ6LTnOw4yFMO+U9aEF4Ti70a9bW1u4cOHCbakACD7A\nnleKqdnq+mirylZvLTYANetqHtPn+mnqXpOBuU5hA3XHcWShma7QzCEDQNwrzWhWx9ndkq5araLd\nbqPT6eD48eM4evToWBxMr9dDq9XCzZs3Ua/XUavVZEGd5dPK05G/+tnNsWfKoVMrft/N416ztpd2\nYJ5zNwAzqEzbJGPTRt4L4gUu/OMOPMx612w2hSoYDAbY3t6W7HjchZ6gQU0wnU4LoALjod3cMYbg\nSoqC2kyz2cQnPvEJxONxlMvlsR176MHCRP18B3rRlH7QjUZDdgRizg1qtBrcNJAzZzfrTu8W5loh\nCHBBlM9B8Kf/OhfbwmqofmJmFiTo1ut1rKysoFqtimcG60Sg4r6kOqxc31NbzmZdg2hO26Km+Tlo\n0gqa2EyL0OZxo+ujrxmNRqjX60J9cOHRdfcW27mxBb1MdnZ2sLm5iZ2dHdRqNRSLRSwuLiIej2Nj\nYwPnz59Hs9mU+2nA5iRrbsCt6xbUHocO5MwyNhqNEI/Hx3JfaLOcYuNi9YvQn7Vr2H5NwKCdOcJ0\nOD/Rz6OP+X33s1L2W4+wEolE8Pzzz+P48eNYX1+X/RMrlQqq1aqkNKUGrf2t/ThXALKyTy2bA4eB\nOxwAzPXdbrcxMzMj2s3U1BQSiYSYsdqLhomR6CamuWNuw0YNnG53DFPXnDlBvN/vY2ZmRtq10Wgg\nl8vBdXc3E6YfMTV88qzU7pPJJJLJpLhMclMMUhlBfQIIfqe9Xg/FYhGVSmWMMmq1WnjllVcwPz8v\nFpF+T2wHHtdgYlJOmsLQ2rs5drWlZoKT9hbSZdsUPS8rXfv88zvL0PUxk1npSZnfbdhAK810KdXg\nzw26uXZDr5+VlRVUKhWxAPWzmMK6A3t9gOX7yaFTK36A6mVuhdG89Qu0/RZWghpwEo08rHg9Y1gL\nxqtMPwlTz8FggOXlZTz33HOYn58XXpnBJVwo03SKOXjM+pianNaiXHcvCRHbhK5/jrO3DVu325XA\nFg5IAijvSzOZA46DkvchyI9GI4li5ESkOfvhcIhsNisDjoDHXCwsXyfC4jMQBEhJ6ChCWjHU9szc\nGvt51+l0GpVKRdzagN0J5/z588hkMmPgrWkDU/Gxgc4k4zBMff2UFdPy83pewB7ww+NeiqG+zixL\n/zdTJ/AzJztSWIPBABsbG5KrptFoWNsqjLYdFj8OXSO3fTbPCdJIvQDbPN/sWGE7mZ8EcVdhxKZh\nBHVY/f2gtO0gWVhYQLvdRr1eRzqdRi6XG1vMMcHLHDxBkyIBmADO7/o6atP0AiDAMrMhtWEN+ixb\ng6dXtsJ+vz+2yQL7kY6QbLVaSKfTY9yx1ipNy4PauF4M1WkpgD2Q0IErrIOtncJ855oELZl+v4/l\n5WXcvHkTMzMzY/nDbfyvLk+3UVgaJKzVa56/3/5sgri+t9kf9XF9X1MJ4X/TsjA/6/KGw6HQigBE\n4/cC7qBnDtOGh66Ra7Fp0TwOhA/OMWdXvwYMU6c7kYOYLIIGc5h77McyMOXGjRviVsXFPAbXMCWr\nrpP2BHDdvQRKfhMQuWXbwAJ2AYc5S1zXlchEctwzMzOIRqNCI5Cq0BtBEKxNTU8vUppam3aX1Ple\n9DnUzLTbH7Vw/aefFdildtLptEyE5Mtt4vXebW3a7XaRSCTQ6XSQz+exurqKy5cvI51Oy3No2kvT\nHqb3hO1eXlajCfZaJrG+9XE9pr2AUAOy+f5soKx/N+kLc0Iwy7X1Xb53TanZ6F0bcPtp7F6/azl0\nIA9jKpnH/V64eX1QWUES1IBhoq4OUsJoaAdVru2cpaUlnDx5Emtra0JFkDrQmwYD477X1DbN+5n3\n1XQIfzffOwcd759MJoWTnJ2dFY2b3ijAnp+wmaGOO79wkuHvpDp0iDoH5uzsLBYWFpDNZgWYCewa\n8IN2itEZFXUUZRhazasd9X++m0QigXq9jjfffBPtdhsLCwsSVej1Hvy0Tr4LL8XJq95etMckWr6X\nmGDrZ6Gb1/G/nphtmr35jObv7EvmvTTXbV7Lcm11msSieU9TK0GUiz7nIMBuP3KQ9w0yqe+kHmE6\njOPs8sLz8/O4dOkS5ufnxxYlzUUr0wzVSaPC1tWmQQEQjwGmhm02m5iZmcHMzIxoQfRGAfYWO+md\noWkWLkwyx4njOOJOSE8cnrezsyO+wuYmEToTnuvu8fu2BTbX3U0ixl1paIrTcrEFV5ntYwNb/TkW\ni6HVaiGbzeLixYu4fv06crmccP9B5QLw1FL5boLqaIoXmOvybf3cSxvn75o6Me8Xpm5ao9cKSFD9\nTdrFa30oyErxGxfvOWoFmAy4w5RF0Y35boL6fjSLIO3L/P5uUETArulIjY50Cl0ICeYcENqDgR3b\nRheYddd+zZrT1IOA1IherKQPOYHc9MklbaI9B3TZOpcKXRKnpqbE+4bfSb1kMhmhZ4BdICcfHaSd\n0johYOtFY/oU+1EQXt9NsOP6xdraGq5evSr1bDQaYzSKvpZt5QU6Nu3Z/N1Pk7RNBn7XhNVKzXFj\n1t9vXGk6zOxv+plsdJONftG/mX3Xqy53qnDeU0B+UADrV85Bg/jdKC/I0jgs64JpOvP5vKR/5Y7z\ndB0EMAaOFA4WLTbtzjSPTa2GUZk6lJ/g+9RTT8mmxtoDA9hts+FwKJq849y+fyTP09ud6cT+jPhz\nHEf8xikMxwb2siT6aZGDwQCdTmfMHZFrDq1WSwKZtPhN8Oa9XHd3n8pCoYBXXnkFlUoFhUJBMux5\nafUELC/Q8QLxsJbWJMqYOeF6tadtYrDV11ZPc+K1AS/FzNxo1tVMicDjZh+z1VMft1ktQe126DsE\nccDpVXsdIs3zbOL1Qr1mQPNl2fjtMKaYzayynQf4+wPbZn0T0PSfDZwIPH719XouWz3NZ9PXMYf2\n8ePHMTU1hU6nI++N3iaxWAydTgfA7q489DNnuDezxHElX6eQ1duj2cCJ7yyRSMieitFoVMB1YWEB\nU1NTaLVaou3y2qmpKWQyGZlsqJ3rAaUXbVOpFE6ePIkXX3xRaBPX3aVDuAkEAOTzeSk/kUiIVt5u\ntzEYDGRRNpvNot/vo9VqYXZ2FtPT02g2m+JbTj94Aq3ZZ23tQprHdV25L59jenoa2WwWa2truH79\nujyX4zhjGyLwGp1LW3uv8J3Y8o+YfUovbuu66Gt0/dm/dI4Ur/FnTjj8TDG1YT/t2ya2TTb0NY7j\nyDtLp9PSjpyISc0xlJ/rOKTwzI08vES3jU2r95JD18jDzNJ6IO+nrEleqq1zmsd1h/HKjaDBwbzW\n1Gr8Oq9Nu/ATr3oEXRs0gIC9hcCZmRkkk0k4jiOJgMyFTfLRXMFvt9tjUY7c4Jg5RbjYF9TZGfhB\n1zqCzdNPP40TJ07IxKEBCtgLPWdATKfTQbFYFCAl4HKiYHAOBycnTO72QwCl6LSmpJCazaYEIE1N\n7eakzuVyyGQykmuDe3h2u12USiUBd9v7ND+zLbRnBN8Zt7K7ceMG2u02EomEAArdDsO4zvqBYtB1\nfkqLH9VhK+teENd15d0wtw/pO02Rce1G0zVBKZ61TGK1UO6JxU4T3EwtzDzfpnHrc/bTEGY5FA3E\nfhq0WcegY/o3mzbgZ1bt555Bg8E0B20DmEBQKBTEXY6uiAAkKpM0Q6/XQzablWCZarUqATfUZBYX\nFwHsJtfnVmS2ZzE1LmqQrNcnPvEJFItF8cZgIixOHtQW4/E4Ll++DMdxUCwWBZy12yO10lgshsXF\nRaysrIgfORdXqfFzgGrti+Afj8eRz+dl4qBWxg0FWCc98HWZXu+MxxnSrS01rUA0Gg1cv35d2o9u\nm6Zy4SVB/TbMuaanxp1ywVoOsqwwQkVDuyrys87eCezlPzctkLsl94xG7jXz28wcWyealK8Lex61\nP96b13mZeEHlBmnftnuE7QB+9Qhr9fjVnZ2V3iGVSkV2t6Epzi2xqHV2Oh2JeDt69Cjm5+cRjUYx\nNzeHwWAgyftfeOEFvP7662MUgK0uegMKAvpgMMCpU6cEMHXOCjNVruu6OH/+PM6cOSN5XfhHXpy8\nPzdkqNVqYk6bPubsG5FIRIJuXNfF66+/jrm5OTz++OPIZDIolUoyUXDAkycHIBsUVCoVtNvtQJBi\nG2mLUeenGQ6HWF9fR6lUkiApaunakrSVa2r+XlZdkCJj3sfLqg0ruh6TWqoHIcQCrWHT0kulUkgm\nk2MWHz2WTErUT8znCnvdPaOR838QpRB03A/Qze9hc5xoYPECc6/7mQPGa7IKMmHvxAIIYx77RbNp\ncZzdhb4jR45gc3NzzL1ubm4Oa2trmJ6exjPPPIPRaHefwtnZWczMzCCbzUpYPf8TyK9evYr19XUJ\nf/d6d647ng+DmjkXJLvdruyTybwplOFwiEajgZs3b+IjH/kICoUCqtUqarWa7MxDjZjc+61bt7Cy\nsoJjx44hl8uNhdlT6wcgfDvvo/e51Im/SLXo3B7tdlvqTJ9yWzi4be2Ak5XjOKIxMjjq2rVrGAwG\nSKfT6HQ6kvo3SCP369PmcS9wDrIi/coOc94k1IzX/Seti6ay9PuMx+M4ffo0MpkM2u02bty4IQoF\nlQpaoJPUdZLJ6tCB3OwQJqjZXthBUStBL49lhaF3wpYZ1AHN5/abLPy0mknrZes0ZlsSFHu9HpaW\nlvDCCy+MmZCrq6uIx+N47LHH8HM/93NYXl7Gt7/9bdlYlq51qVQKc3NzOHbsmGTfm52dDcz6B+zR\nCfTt5rnnz5/H2bNnkUqlRKPVdU+lUrIQu76+jtnZ2bEdy83ByeNMaKV/4z6fpGOAvT05GRTy2GOP\nAYDkq2Z9O52OnMuFU6b15QRFjt3r3fM/34dOs0pLpVaroVQqIZVKyUKy5vq1J5C+lwnGQWPMqw+a\nfccL3CcBY1P2Y4XfiTAGgXQW3302m8Xp06fhOA4ajQY2NzfHFCMvi8Ym+7Uu3lPUiheA2zrNJOKl\neZq/6ft4Abz53U+75Ge9mm+b0EwtVF8b9Oz76UQ28NAWSavVQrFYlEUz1oO5REqlkoBgq9XChz/8\nYRQKBWxtbaFWq2FxcVE8VpaXlwEA29vbvua+rj/BXCfI+uY3v4kjR46gWCyiXq9LnchHc4Fyc3MT\ng8EAs7Oz2N7eHuM8udlEt9sVTb3T6WBhYUE8ZYbDIVZWVpDJZMRNEdijnah9NRqNsZB8anP9fl8W\neMmnMykXAV8nufLTiE3gZdt0Oh2sra1hMBggl8uh1WrJWgD7kd9CpwniXuewX/idY+s/5vewSsa7\nRaF4iV7Up6XFjTpoQfEzsGc9hl1YNiUsrQLcA0BuSlDn8XqwsJq42Zn0f1P8zEb+ZnNh1M9g/h5k\nttqew08jDzPYwohtsdMsS+fSLhQKwovTdY4m/fb2NuLxOE6cOIF6vY6zZ88KoG5vb6Ner+P69eso\nlUqikW9ubmJubg6A/4RCrdPMffLd734XP/qjPyqh+ul0WiIzAUiyr4sXL6JYLGJpaQk3b95EPp9H\nLBZDrVZDr9cTD5NarYb19XVsb29jdnZ2bGOG5eVlFItFFAoF2RmGQE4tfWVlRXb9If2ztbUl5S0t\nLcmWbgRXavpBg97UojU48/1sb28LgGxvb2NmZka0cC7ahnWHmwRA/SaeOwXigyjjTqTb7UqeGmAv\nq+XOzg4qlcpY+mLSKmxzWk37kXueI9eDg0Ed3MHb5vbH79SitPsZPShGoxGazSbm5ubGgjL09YB/\npwjS7v0ANAy1Yt7fBHsv8J+UO7PRIubvftaIKdPT06jX65iZmcHi4qLUQyeh4n3+7M/+DMlkEi+/\n/DK+9KUvwXEccbnjn+u62NzcBLDn503OWddB15OTBQDRkKPRKJLJJP74j/8Y3//93y8UC6kTlt/p\ndPCd73wHDz30kHii0HoAdikjhuTncjlcvHgRjuPIrj1M08tFyhMnTqDdbgOApCrV9BE9dGZnZ1Eu\nl3H06FFcvXoVruvKRhekQrhjUiwWE21eP7fuK9oS00nEeG2tVsPGxgbi8Tiq1SpisZjUExjfn5KT\nBjlcra3bPLZs8R0sy8a9+1EuNlrGNn7CWNthx4SXhFl7YnphtpveserChQsoFotot9toNBoSa8B1\nIjOHjtdzcHIAvPdksMmhc+T6sw460P6Z7Dw0XSnajNFpSaenp8Wc9LqvH3e135k/rAY8KfXjx0Me\nlIQZCOR56f+cz+fHAlgYUFOtVvGNb3wDU1NToqnQl1nfy+zYYTQW893pPrOzs4Ovf/3rKJfLePbZ\nZ3H27Fm5540bN3D+/HlcvnwZH/7wh1Eul2XPRQIjXSkJqr1eT7R/Wh/9fh+dTgelUgmrq6tj6wak\nljixNZvNMUosm81iaWlJnpveDQDEPZEmu/lObH1gOByKdxCDkUj9aA3Qi4YJA5ph6Q+vd2Ur1+ve\nYco5TDGBlX2W6ZJXV1dlMZsTos69czcT7N15Mu37cl/uy325L4cqh66Rc6aips1ZS5vEnM11cnZe\n7ziOROVRm0mlUhL6zfO0mBqGF/Viu9YUG92hv4dZiAySIB4+jHjVU5dno310/emRwQW7paUl1Go1\n4YmnpqaQTCZlv04AY4tseos1MyLWtpjrVU/Wy+SI+Xf+/Hn0ej2cP39eLLtGo4GNjQ089NBD6Pf7\nuHLlyliQD3n/SqWC0WiEcrksKQU0Z02r5ObNmxgOh8Lr0wrkZ/px0+1vfn4ehUIBCwsLqFQqiEQi\novW7rjvmOWOjH7ye3+ReNzY2sLq6KvSjbheTEtTfTRPeL+uhWR/bWLKdv1/tPsz5B8mdB40Rk7Lk\n2oruk3oN504WPMPKoQK5TvepO5IXJ6Sz6elIPfrPavctbaJqMQeCFvNY2MVTv2NhwPtudOxJyjJB\nw8ujwHVdWUzLZDJjC4b8nRtNcOGH9IXej9W8L7+HAXJbvhn+pw94uVzGG2+8gYsXL0rdGKzxxBNP\nyCbMzPsyGAzQarWQTCbR6XTQbDaxtbUFALdtwsA2KJVKiEQiEpmaz+eFb0+n00LRTE1NodlsSkbD\nwWCA69evo1AoiHeNDiQiPeO34MvfotEoms0mMpmM0CrLy8vS3vr92Ux7M9hNUwYalPQxL7H1c5vS\nFXSdTd7NBU6/8Wnrc/ozUzrrtiRW6ba+G3KoQM5wYe00rzlxil6QMTsHgzcI3q1WSwIseI4W/TJs\nnc12jp+EWRi1iR/Ye93Dq0OFkUksAy/NSUcmjkYjFItF4XiBXb/odrstFpbrupKTgmlgbYOA9Qs7\ncZqAY0ZvMo9Lt9sVn2yunUxPT2NmZkYyKNLtsN1uS+KpjY0NVKtVSTegt1/Tiba63S4qlYrUi5MW\nw/u5GN9ut2WXdcdxcOXKFXFfnJ2dRT6fH3u3XPD3spL0++h0OpL3Zm1tDeVyWXh93WZBfdl8F15g\n7lUXswzzmPkc++27Yeq/HwmjcOn24Hc9EeqFeo1VtgRjBy2HCuRMuETKhP6zGtC1FsfG0K5e29vb\ncF1XAi0ajQai0SieeuopoWf8tPAgrTys7Efz1gPDT8LUO2z9TC3Cz2S2ic49XigUxnJMMGJR72dJ\njZYTNjVDc4BrDSboOWyApn9jP+IkAkAyBW5tbUlQTzabxc7OjviLc8GxXC6j1+uNeY6wjyYSCezs\n7CCZTGI4HOLKlSsAdvsy+3Ov10MymUS1WpXzmRVvenoalUoFtVoNs7OzmJubu40eIX0T9F4Gg4FE\noLqui+Xl5dvM/kn6jW1i1ZOAVz/0shq8tPKDtCjvhnjVzwbkGsy9ygnbt+9EDhXIOdjoJsagCJrP\nvV4PnU4H3W4XAIRT5IADgEqlAsdx5H+73UYmk8GTTz552/1MM9EEE56jj90pqE8C6F7i54s+aZ28\nNDNbG9ieg5TVzs6ObK7A98XMfTqFLPlnzf+agMH/YbQWbbbSXU6DTqfTgeM44hpGqdfrGAwGePPN\nN5HJZCSLYSQSQa1Wg+u62NjYGEuGxGcjp69Tv3JgkhenyyCwC7ClUknC7wnU3IuTAz+RSEgeclIv\nelMM3R6mlci/eDyOUqmEzc1N1Go1yalCNzkNwmYZQVr7JKAbdN4kWvx+zjkoCRqvZntqcDcpMf6F\nTVJ2J3KoQH7hwgXU63Uxb113NxJvZ2cH3W5XXHnMcFf6D9OveXZ2VtzbuHiUz+dRq9XkXiaIa/ED\n9EklTIe+kzIPQjO3XaO1WtugZofs9/uSbIopWPnb7OysUC1sa+Ye1wvQNpMzbEe3AZKeeHS61mQy\nKdfRIuBmGNFoFK1WSxYpZ2Zm4Lqu8OYclLQyGLcA7O0GxERXwK4Wzf6WzWbFMmRZvK/jOGMbN3MR\nmBGdBHOvTQq0RUMgd10Xb731lviT08yf1Nr0A6n99LOgd3pQkwRwMDx60NgyLUnek+wBnQFsm3ff\nbUvkUIH87//+7wFgbLGSZqXfLiZsUPru6tSlyWQStVpNBgwj+xgsxLJI6Wi5Gw2tTWbT2d98PtMs\n1hQS81/zubgFGX25gb0wcGp3ZpAOsOdRwZSx3MTDlnbTpqFReyS/Sw46EomMRSrq5yGQ2RKIebW5\n16ByHOe2nYh0OfSqIYiz/XXQDLXj2dlZtFot5HI5AXdSKqwvrQtq+vTAofZNa5GgTmpF56NmHZhS\nl/2AgM7JTns3tFotLC4uysTISYaLydFoFPl8Ht/97nfx1ltv3ZYCV3uBaYA2k3Hp/mhrd7YvtUpt\nkdjeoQmoNqXJnLyDxt27wZHzPrZnsYE3cHtwlAngZv30Qn3YdBRmXbzkUIGcSXxorlOD8+KTNJBT\n9AIataZut4vt7e2xTHr6792YISlcLAP2Apr0KjZ3e+/1erJQS7pJZ9DjVmqRSER2b2fqVIIpgYqg\nyvJrtZpomswNTtAnj+yn5dreAfli8syTJM4/THHd3YRSCwsLWFxcRLlcRrVaFddJneaVEykXPak8\nEID1gNQWjfau0u3Ifqe5dxt/6rru2IYWAGQbONfd3b2o1Wrh+vXr2NrakslEp00F9jRFsw78bAMT\nE6wmpb7CvoO7TTXcDZnEwjCf0WZVH2Qb3DNATu3R3G3Gi7slJ8UOaZqEzOPBjr1fEzFIghYwTA+a\nZrOJWCyGhYUFZLNZDIdDvPXWW2g2m7IIRlqJIM7oRPqqkuPlwhl/T6VS8lskEkGz2UQkEkE+n5fy\ngL1k+DqDYFCn05/5jtLpNGZnZ7G2tiaJoA5bwrxfUh6O4+DRRx/FxYsXUalUZIs4ctRUDBipaUbo\n2YCQprU5OQJ7Whv7urbQTOuC1gEnab2YzFwwN27cwPb2ttRPm/qsp40n14oPxQuo9XGzj/hpnzZt\nO+z173Xhs9n6YlC77VcOFcjNDgdgrJN58af6HAI6zd1IJIJEIoFyuYyHHnpoDMjNe7wbGjnNYT7H\nqVOnsLi4iG63i0uXLuHNN99EvV6XIBqtIbPOpIpYRqvVEsAg4ADA0aNHUa/XMT8/j5MnT6JUKmF9\nfV20Sc37clIwc9v4CTsh6RvSK1euXMH8/PxdXZUPKxq0vH5LJBLodrtoNBp4+umnZbKtVCqyDykn\nQ/rNd7tdcV+07TFpatZaqaCwvdnmOmjHFFqpus9ykimVSuJqSBqHFBAtPpMq022g/5viBbZe7el1\nzEsRO4h1qHtZNIhrMLf1y4N8/kAgdxznOIDnACwCGAH4A9d1/y/HcQoA/hzASQDXAPyk67q1d675\ndQC/AGAA4F+7rvt3HmXLf82navNQN4atM5GHNHOw0GcX8KYLDopX8xNqrwAwNzeH+fl5rK6u4h/+\n4R/w1ltvwXF2vXPoaUBNjaBAOobgQv9oeofobHlXrlxBPB5Hv9/H0tISTp48iVgshpWVFSQSCWlb\naufxeHwsCVXQs2lqgDnBmdf7bgY7TCJ+75R9wNyiLpfLIZvNYnNzE9evX5d3AmAMvKmp670Y2U95\nTPczkz5h25FfNwe1vo7unaRNuLi8ubmJGzduYH19XcCa2rgGcIqN9zXBxWwj/Rx6DNk+63LCjKe7\nQSvcqxJEUb7b1MoAwK+6rvu64zgZAK84jvN3AP47AF91Xfd/dxzncwB+HcC/cRznMQA/CeBRAMcB\nfNVxnIdcy1v2WlwIY3qYlIo2ISORiCR3t9EqBzkTBr2MRqOB06dPA9iN/rt06RK+/vWv4+LFi3JM\nB9W4rju2sYF2SyOVtLOzI1x3v9+X6EJuvcbNEz75yU/i0Ucfheu6uHXr1hgAaW3QKwrW9qxcaCOP\nPz8/77mmcVhis+r0b+S/W60WhsOheOEsLi4ilUphe3tbNkKOxWISpq8XMk3qwLI1l3wAACAASURB\nVCu7HS0nM6iN/00KBNhrZz05coF6Y2MDt27dkh1oeC+9QYYuxwYYmoLxok20BWu7/nuRWgnLkXtZ\nQEFK6Z20SSCQu667DmD9nc9Nx3EuYheg/yWAj79z2h8D+DqAfwPgMwD+k+u6AwDXHMd5C8AzAL5t\nlm2anroBvKgVrSnoXL/6fNfdTY+qw6pNLeSgOlIQgOXzeQmKabVaKJVKOHbsGM6dO4fV1VW8/PLL\n4rbEAUy3S9afnivxeFxc/lKplET2ra6uSl24ue/bb78tZvaDDz4oOZO73a6UoTlzm5gTHrV5mvHD\n4RALCwuIxWL3jFYepJEDezu91Go1oVVarRai0SiOHj2KWCyGeDyO7e1tiVnQ3h3aYrTdT2u0XHA2\nc6jovqr7qZ5oGWnK81ZXV7GysiLpblkfYG97OdbHzytKjwGb26m+3gR7/TmIWrFNorbx/l4DdC/x\nej7T2jJ/92rLSWQijtxxnFMAPgTgBQCLrutuvFOhdcdxFt457RiAb6nLVt85ZivP935+Zok5ANjp\naPIz+ZFXdNtBAXmYF8C8Hf1+H3Nzc3jooYewtLSEer2OJ598Er/1W781tsuIznlMnjYajaJYLOLY\nsWMoFouYn59HJBLB1tYWPv/5zwMAFhcX0Wq1kEgkkMvl8MILL6BcLuOXfumXcOLECXQ6HVQqFeHV\nyZPb8qB4iQ5XB3YnKi4e0r3usCVIW2y328jlcuj3+9jc3MTs7KzQTLVaDYVCAel0GslkEisrKxJs\nFo/HxwKgbP0K2FvU5GedytRWH/ZdfT29VriQDQBra2vY3NxEoVAQDxuK2dfNFLa2e5p1tik8uj0n\nHS9hwPz9LmEop4NgCEID+Tu0yv+LXc676TiOefeJa0PuV0fQaU8Udk6+eK21uq4rGgs1WpaRTqcl\nF3QqlZJ9F+kdw/O98l/rjmZqLPydden1esK30r2PVAjrxHwcBINGo4F6vY5cLocnnngCv//7v49f\n/MVfxNraGo4cOSL14n6Oo9FuEqpf+ZVfwblz5yQikZsf/MVf/AUASLQlAAmQunHjBn7jN34Dn//8\n51Eul7G6uopCoSBaJl0fvSZN3RbcOqxeryOfz6PdbiMej4sLIhdV9QKzbq8w+cbDTu5B15vaJPuY\n3qC42WyiXq/j6NGjKJfL0nfo0cKcLO12W0Ls+QwmbcN7cUKmxmzyyvoa+qSPRiMJ7On1erLBRiwW\nE44cAJaXl5HJZBCLxVCv15FKpaT/Mwe2dm3lu9Agbb6TIA1eR+PqZ7Bp97b3p497afN+fS7MO59E\n/CZfL9FUlD4WRAVrjyQAYxOv18RoUmpe9b7tXr6/7t10Grsg/ieu637hncMbjuMsvvP7EoDNd46v\nAnhAXX78nWO3Sb1eR61WQ71ev203n6CZjIOG2o5ubGrq9Ls1tXeK1z3Mc2xcu55k9M4qmvfUQRp6\n0Yw+4q1WC5ubm0in03juuedw9uxZrKysIJfLIR6PY21tDa7rykJnp9PB/Pw8Wq0WXn/9dbz44oto\nNptIp9OS36TZbAqoMgtfqVTC7/7u7+Lpp5+WNKpsQ71Y6qcxAhBLgW3NgCK6ebJMr3Z8NyTMPfVE\nq2kTHue75vMlk8mxaEzmaqF7qMlL2waoBkD+znvp/sXfWSYnna2tLYkx0G67wLiCw2Nh1jzYRmG0\nxrDybr3nwxBtXXmBuA3D9tu+Ozs7aDabaDQa4vnmJWE18v8HwAXXdf9PdeyLAH4ewP8G4LMAvqCO\n/0fHcf49dimVswBetBWay+XExcoMiPAyCYHxiEECqAZs/i+Xyzhy5MgYXWGu6utyddmmduIFcpov\n1UEkBDyGagN7mfP4x+AlYNd18Nd+7dfwuc99Dpubm8hmsygUCsjn84hEIlhbW8Nzzz2HcrmMq1ev\n4vnnn8fOzg4WFxdv8wcejUYCUJFIBMlkEn/+53+OX/iFX8BDDz2Er33ta7K7OoOIgjRyPRnqZ56e\nnsbs7Cxu3bol1oBX294roq04hs0DkAVNvle2XSaTkb5DOspWphn1aIK3/tMLlXxnpGR4L07+wO4O\nR0zGxfQAfAbei+Xp7/tpGz+6RYs5Rk0qZr9yr9IvQWtJZp29vod9Nq7VEFf0dn231S2oMMdxPgbg\nZwH8C8dxXnMc51XHcf5L7AL4DzuOcxnADwH4X995qAsA/jOACwD+PwD/yvUYzUEgGVAv63dtAm5t\nbckMSk3Fpl17zaL6u+0+wJ4vuzafqO2RuuDgpRZnamHVahVvvPEGPvKRj+A3f/M3ZXd3Rl4CwAMP\nPIBSqYQvfOEL+Pa3v41oNIqZmRlcv35d7kVvCvo902Vtbm4Oruvi937v93Ds2DFZnKS2R5PP9sy2\nweo4jrhLDgYD5PN5NBoN37Z6NwHdz9LS1oXjOOK5YmrCnKi4HygXiKmlM6JWW1vaO8UG3lqbI9Wn\n78f+YEsfsbm5KQunrBstO9Zfb32o3XH92skmYfjwoPc5yfv2U9ruJTGtTb43/rdZXbbvZhn6+34l\njNfKNwF42Wn/hcc1vw3gt8NUwGsWt81yGqSpxZgNyN/pgsjOzeRFJoj73dNWB5sppXktUil0DWRe\nD2DPF5iLgrz/zs4OcrkcLly4gB/5kR/B5z73Ofz+7/8+CoUCOp2ObOTguq7sjk7XP4IJANlrklQM\nLYB+v4+TJ0/iy1/+Mn7+538ep06dwurqKlKp1Fib6Gc234mpOVJr7Pf7KBaLY4ugNhAIq2Xd6QD2\n4lfNcvkcbDNt1RHQ2afo6qnpDPNZ9URqaue2Ad7v98Vjhnw6gZqfOekDu+kXksnkmOsnOX9OELyW\nv9MS9BO/9tbaOdvFa9x4TZyTaNZe4+9ekSCFL4jb9mqLg3jOeypBhtlI5oKS2ShaizHphUgkgvX1\ndRlU9H8m4PhRCeZ99ORhqyv9sPXAHwwGWF9fxz//8z/LAMvn8zh69CgWFxeRTCYlT7Xj7KaAvXXr\nForFIn75l38Zf/AHfyA5s4fDIarVqgB0IpFANBpFNptFJpPB22+/LXXlzjD9fh/1el3akFTKpUuX\ncObMGVy/fl0CizRVop/N1hY6MyCwC4hLS0uSfMtmKb2bprLXRKRFLwb2+33xF9dKAScqDeQETr0A\nqf3DtRusjWahsE0IxjrBFrAH8gRkXsPNU2gJ6QVNTtw23/mwbeV1jtdY8dMo34/Uipelyv+2MaRl\nknczqRz6np36vxY9ALy0RfOYPndqakp8ybWpbHsBflq5DZx0OZoX19esr6/jxRdfxKVLl4Q7zmQy\nuHXrFs6cOYNz586Jtwu36hoMBuI98WM/9mP4oz/6I5w7dw61Wk0WLuke9+lPfxrRaBQXLlwQ7iyV\nSslgpyavd7lxnN3daZ544gmJCo1Go56D0RxQXBzUQD4YDDA7OytbqRHE9Pt4twell2WgzWGCJxfF\ndfpaXYZOTEbw1P3MZkJTkaCYYK77Tb/fR6/XG9sOjtvNcVGVMhqN5F3b6sq2JrDfiRwEtTLphGKO\nw3sRzCn7mSyDrr2r1MrdliDTzut820Ayz6nX62OeAbZzbWXpYyY48Tc9YWiTm7z2rVu38Nprr+Hc\nuXNy3fr6Oi5duoT19XUkk0mcOXNGKBhuhECvlU9+8pP467/+awwGAzzzzDO4evUqGo0Gjh49ikql\ngosXL2J5eVmyPAK7oMrdYnT+jXa7jWQyiRs3bqBWq4lbJjc9YJSgrW31ZzMFKjVaukPaBp+XRfVu\niZfJT+oCgLixkhbTbpJ0j6XlQs3YtAQ1SAe1A6Xf70vA0dbWFhqNBnq9nrxD3c84kTDPOicCMziJ\n74nrIO+GHNR7tVE29xqYm1Qjj+m+4KV4+j3Lnbbh4Yfi3Zf7cl/uy325I7mngNw22+mZigs65qIS\n+UytHdM9q9PpIJfLjQWpeHmt2P40XaJdC/l7t9tFPB4X323yr9/4xjdw/PhxuK6LbDaLbDaLUqmE\ndDqNzc1NfOUrXxnznGAwTb/fx1tvvYUHH3wQ586dwzPPPINPfepTOHHiBHq9HiqVCqanp/Hyyy9L\ned1ud+wvk8mg1WrBcRzR+Eej3R18pqen8dJLLyGZTEpout5Jx0tc15WFVGDX2mGdR6MRTp48iWq1\nikwmMxbQRE+dsH7NtrUITYn4vSu+Jy/Kjpoq3S65TkEfXS5OA3sBRNy5iompvJ5DW2hac2fEKH9j\n/+WCeLVaxZUrV3Dr1i1Jpct2YFlMjQDspeBleWYd2P919kMd00Dt3Y8u5DU2ztfW5jZ6yba+xTJN\n/3lzIdirTkHv3k+8zvO6r18ZfnSJV9+jxafL4HuyuURPKodOrfiJzRzxM1HMxhgMBuh0OkilUmM8\ntrmIF6ZsL+H2XBxgOzs74oqXTCZx/Phx2cGIkwo3jX7ppZfwgz/4g2i322g0GmLuc1Hx4x//OP7y\nL/8St27dwvLysuwURBCg+xkXUx988EHJ3sddhKLRKHK5HIbDIT74wQ/i2LFjAq663l7mIMVcrDO9\nPLiVGyMMuShH8QNYLX6UhB/Vxs+m+5/5XZdDoCJ40svHjEr16hNm3bwAyXac99b+6doDRQMxn80P\nvLwAMaitJwGQw6DG7ks4uac0cpt4Aa5Xp9IdfzQaoVqtyvkEL9MNMWzZNuFAZHpTAKhWq0gmk1hY\nWMD8/Dza7Tba7fZYCoJOp4OXXnpJwJ15sKlBb2xs4Id/+IeFDweAmZkZ9Ho9bGxsoN1uY35+Ho89\n9hg+85nP4DOf+Qw+/elPo9/vY3l5Ga7rolAoIBaLYWtrC51OB08++STm5+fHQGN6elp81f1Aw4w8\ndBxnjEuenZ0VbwqdM4ZgoUHdS+P2e9de52kfetufCW763gyoISdNzdemTdkA2us8m8eK1tTN48Be\n0isqG2GsD7OvU1nRKZHNepqaozkB+U1I+n3cl3dPwrT3Pa2RU0wTzjzu9dlxdncKOnPmjACPzoVi\nrvpTJtFUGOlHjZRbcmUyGSwsLIgHC7AbyUpAY7rUCxcu4EMf+tBYjvB2u43z58/jB37gB/Doo4/i\nrbfeEqqIZT/00EN44okncOLECXmOmZkZpNNpyY7HPDAzMzMYDAZYWlqSpGJ6D1MbQNjaXB/XVJXr\nuigWiwLg9IRhGxNg9K72XhaAzv/hB0Ssg82M13W1TU5sL+6+1O12xZOFC4T7sc5YP68FMX2cwM7o\nvWg0Ku6IQX2P/dPrmYNkkmcLUprulcXIO51c7rXJSffbMG18zwK5F5D4HTNfRjQaxfb2NgAIoGgN\n0+QYbbx8kJh5LlhGoVCQvCYbGxsAgKWlJaF6uNHD5cuX8aEPfQjJZBLNZhPb29uywcHVq1fF44S+\nxbOzs/joRz+KRx55BMPhEKurq7hx4wYAiHviwsICEokENjY2hI89ceIEstmsWCjkx+kt46Xx2kx4\nauL6f6FQECCkB4zrumMJ0bzK1O2vOVybFu2V58JmWWlQ1J8JorQY6GnE4CrTWguyFrzur/+bdBSP\nMQ0A+XrSc34Uk/5uDvigOuv6TQLC5vn3GvC9X8TsU2Ex6Z4FcoqX5gEEuydGo1HxJadbmVfgi/7u\nVbZNOECpycViMdG8Z2ZmxpLdJBIJ0d6np6clleorr7yCdruNUqmEUqmEdruNra0tvPHGG3BdF+l0\nGpFIBJlMBseOHcPCwgKazSYuX76MGzduiIY5PT2NM2fOYGtrC5FIRFLdrq6u4vHHHxctnW3BiS2R\nSIzRK14gQEuGgEEtli6Impc3F9U4kQaJqcWaEZLaxcu2eO2VAExPDLR8eG40GkWj0UAmkxl7Ntsk\n4NUXzXrwOi/3RGBvM4pYLDYWS7Czs+OZEthUWLR2bjtuXmuru66X3/vXv90rmvj3goSZNO95IAf2\nT61MTU3J5rTkS3XGONu1k2rl9D3WWQQBoFaryS7n+XxezuW9U6mUDNavfe1r6PV6Yxv98rd4PI5y\nuSzmd6fTwfLysvDux44dw8MPPwxgN3J0ZmYGv/M7v4Nms4mlpSW5Dykbaujk5IG99JphtDmCj6ZA\nmIs8l8uh2WzeBiwEfFs72xYMvX43wVJbV17vzHaM+WHS6TQymYykPzCtCT1R6DUCXbZ+RjNegdea\n/ZbtofO5pNNp0cj91m+8nsvvux/fbT5LGKtMH79XAD1ovN4r9ZxU3vPUykGI67poNBqSXxrY05I0\nEJnXTPLS6fbI/51OB9vb2zh//jyWl5cxHA5lh/lutyt5VwjuzLNy69YtALuDmpQHvU3y+bxkP9vY\n2EC320Wv18PCwgIeeeQR/PRP/zQA4PXXX0ev10O1WsX8/Dzi8Tiq1SoKhQK2t7dRqVRkQmD6W24h\nxkhAvwFMGoLgpAGPXDwjP9k2rjuewoDl8L95TNfDluDMJl48tOkpo7XJTCaDmZkZzMzMYG5uTja9\n1ueaGr95Py3mxKIDpGxtqoF8amoKiURCknMxPa5Zfpjv5r38+nIYIPcD8feThHmed3MymLR9Dz1E\n36vzeKWMNK/XAK2v7/f7SKVSaLVaGAwGwg/TVY8gY2qEpnBA8lzTRJ6enkaj0RiLpPvSl74kEZOu\n6wo4MW0tI/NisRg6nY5sFtButzEajaTu8XhcdhcCIIO9UChITvHXXntNJoHhcIgbN27INnDMopdK\npXD58mU88sgjyGazqNVqSKVScF13LI1ro9GQ1MLk1+ljvr29LZpjo9GQDRgymYws4BaLRVy6dAn5\nfB71el22k+POOgR0+mTrjRrIF9N90aZR6iyWmivXYM8JlXlpeI528WNqhK2tLWxvb8NxHBSLRdnE\ng54rXJsYDAaS+oBiy/FDTxG9GbL2VGJ/Ih2lE5BFIhHMzs5iNBqhVquJBw3BXpdpA2gbf21aneak\n6WeJelEoPG5mFTXrECTvNt/+bmjsmrb1q4eJJV5WHn/7ntbIqRkNh0M0Gg3ZTUUHDXlp5RQ9cFim\nBg8dMEKtfzgcSqIr13XHEizxemrB9AcfDnc3Ah6NRpiZmUEmkxHNnrQNB8/p06fxqU99Cl/84hdR\nqVRQLpdlcjp58iTK5TJGoxE6nY64Ana7XWSzWdlI2HEcSbDEvSldd9fzpNlsolarAQCazSZKpRIA\nIJ1OS/4W/awE8XQ6LeXxWCKRkJwhBGq2le19Mduf2ZHNCZ6dWwet8DjXIUxFgBOI67qSuqBaraLb\n7co+p5y4yOebVAddBMOIXiPQmjq/mxMR65hIJJDP51GpVAT0dX9jmWHEqx2Dzr0v+5O7pbGHeTfv\nCyD3elB2/nK5jGKxeBuQBzW8jVrQmt9oNJIMg67rymRx9uxZGbjMiQ7s5dZwXRe1Wk1oiFqtBsdx\nMDs7i36/j2q1Ku591AqPHj2KZrOJzc1NfPnLX8ba2hqOHz+Ozc1NXL16FcCuRrixsYHFxUUsLCzI\n3pQPPPAAZmdnUSgU0Gw2AexqeKRpotEout0u+v0+yuUyut0ucrmcRH8CEHonEomMJW6ilksgZrsz\nQ+NoNBpbCNV0CbU6trWpbdoWCPV3lsWsg9FodMylVGv8wJ42zMmE92JOcgCyEE1LwnV3F4MTicRY\nff36jFYUqJWblifrrJ9jamo3f/3s7CwqlcrYvcwoSN0/vfqxF80Sli65E2AKqtv7USZxkNDiRYFN\n0obvCyAH7J2Wg1wHBelFKVPD0ZymV+NprwYdatztdmWgP/jgg6IlLy4uihbHbbrIgXNiGY12U8F+\n/OMfR6/Xwze/+U00Gg10u10B3o2NDTzxxBP4mZ/5GfzN3/yNgH0mk8Hc3ByAPY+ZRx99FCdPnpQd\nZSqVCs6fP49Go4G1tTW0222cOnVqLHCJ+duBXX93DWAAJN0uXRV7vR5yuRwSiYSA4rlz5/DKK6+g\n2WwikUjIgmo+n5c86lqjtgG0LbRb/5mh5gRvvXk1y9YaNCkPYG/BlulpOTkxEyEA8bWPRCIoFAry\n2St5mq3/2QBTX6MnLD7X9PS0bN1HLxZgz+Igf+7FwZv3Mr/fDWD9XgJrP7kT+sbWjyaR9zSQmwPC\n9n16ehrVanXMY4Xn2ExVvwU5lqkbm9ypHmTJZFI44cXFReHOSR1QMyew7ezsoFwuIxKJ4MSJE3j1\n1VdRq9VQLBbRbreRzWaxvb2NxcVFnDx5Eh/60IfwwAMPCNVCrbnb7WJjYwPRaBTVahXRaBTr6+t4\n7rnnkM1m8cgjj6BQKGBzcxOxWAzHjh2TwKBbt25J/nOmNNCAqTVVfs9ms2PUw6lTpzAYDJBOp3H8\n+HHE43EsLCzg5MmTePHFF/HKK68gnU5LjhbtDmoDdz3x6nTEbEeml6X2r2kLE7S026R2RXUcR7hx\nTgjAeAi99jwJsuTCDkSTx2bf4eQ+MzODra2tMZoqEoncFj1r9tsw99Xj5E656qC2+F4C+YOip7wU\nAD95TwM5xU/ziUQiqFQqY6AEwDdtq9ZyzAGjRWt+sVhMFqhYtpksihys4zjCowPAsWPHsLy8DAAo\nl8tot9viM/7GG2+gXC7j8ccfRyaTwR/+4R+iUCjg7NmzqNfrWFtbk8HOUPNUKoVarYaZmRlEIhHZ\nDZ7udfF4XHj5XC6HarWKcrmM06dPCx0Sj8dx8+ZN8bJ57LHH8Mgjj8ixZDKJVCqFK1euoNlsIpVK\nYX19Ha7r4od+6Ickj3qxWMSRI0cAAN/61rfk+QnKZlvyWWwWFN8NLYNkMikTg6YvNJBrADaPMTUt\n+wm1c5P6COprZp8zaQxzMtC/abpGh+fTlVOvsRDkzXWD/XDmXsd0/fcLTAcFaO81CauRhwXqSbT0\n9wWQm2IOpFKpJJoj3b68Vv9tZekyNdhTy+/1euLKR02PO/XonBfA3l6R+t71el02UU6lUnj22WdR\nLBZRLBYxGo1w7do15HI5XL9+HW+88Qby+Tz+6Z/+SaJEn3zySQC7yZ+YmzwajYoXzMMPP4xSqSRc\nOjX2UqmE2dlZOI6Dubk5ZLNZoX54Xy52Oo4jfvGcoLLZLG7cuIHhcIh0Oo12u410Oo2nn34a6XQa\nlUoF29vbssGzBlVzMw62jd7BybR+KPF4HKlUCqlUSiZQigZf0ifAuPcR68/MhHrDCJ30S3PYLI/i\nx3OaFgXP14uwflYk31E2mxVrTqcRYBvyOi8u1dS2TXDej0ZvShDv/r0kk3DkXu9hv/K+AXKvxhiN\nRuJLDmBMc/O7Dhhf3DS1OV5HM911XdmeS3snOM546lOtGRI80uk0pqensbm5OZboih4kCwsLiMVi\nGAwG+NjHPob19XWUSiUxxy9dugQAmJ+fRzqdlgCiVCqFer2OXC6HVColbnRTU1PY2trCzs4Ostks\nLl++jEwmg/Pnz2M0GkngETl2YNf9kIm/HGc3hw0zOTIdK9uj2+1ic3MT1WoV+Xwe/X4fx48fF9qC\ngK2ByXXdsTSyJsjwj/WjJq7blK5+WgMmcOpAH/0e6YXD9LbkqR3HGdtkQmvWfn1Q9yv9nRMD+6C5\nLmM+62g0khTFANButzEYDMR1lQm/JqUv/Lj7+3JnElYpDDpnP5TUoQO5l9YV5qH9NCQOZGpcw+Hu\nlmrNZlMSUNErwbyn1l5sGrmuN/OQM2GWpg70oLQ9q74PQ+cJJNvb21heXhZQKZVKmJ6eRrlcRjqd\nxvz8vGRCbDQaAHa1znQ6DcdxsL6+jnQ6jXw+Lxvxcqcbut9tbm6K98wbb7whvu3m4i2AMV91tid3\nFmLZrrubTuDKlSt49tlnRavv9Xr427/9W7jubtZHnQnQBG6vdub3TCYjVIr57qlJe/UHTbfpd8Qo\nWiZVA/YCs2KxGMrlMvL5vCys0h3U5Jj5nnW6ZNvvAGQhWPP6dHnkuwRw20SkXRX1pMVzCe60/Gxt\npNvZFH1cU2CsI/uQSY1pCQNCBz1xhKU13s17+l1nXsv6aUrP7D9+cuhAfjdFL1iR1zW9TYLEb6Kx\nXe8FIOZnaot0O6PrGc18M8EVNWzSNaQNCoWCWBt0YywUCkin0wJQfG4usjqOg2QyiXg8jkQigYWF\nBbz66qsSPETA1c9NzZBgyd+oBWuvim9961u4efMmNjY2cPPmTYlopUbONtLvwMt/XIMKrRcucmoQ\n5EKl9vP2sry0hWR7t/Qt1wBpe/82sR03JyWvfqO1ZXqm0OrQEZ/muVr7N3PNmPTKpGLSieb97gZA\n3pc9sVFiNnnfAzkboVqtirbJ30yO1lyMAryBXGt4+npT4zYDivTvrB8DZpi8iYun/X4f3W4XMzMz\nY7myOaBZVqFQALCrNW9tbSGbzaJYLKLX6wkF0+l0ZCGTO65zdxzSLiyXGjmwB3rU6vmcpqbJdo3F\nYrh+/Tpu3ryJqakp1Go1cVU0n90EWlItpkaotVvzO8vhZEL3SJuLKe+lU9Xa/uuISgCiLWvvGf2O\nTeH9ba6UenKw9R+ez6hcUltcgzHrpp9Na+JeHLyfeIGFWR7bXFNj9+VgxAtvguR9D+TsaKVSCfPz\n82OD31zEsnGXNo1ukkGhB53tJfEYqQz6lzuOIzurA7tRls1mE47jSAg+n49+5NVqFZVKRbRuABLh\nSQqIIE5hCtrTp0+j0Wig3W6LOx7NaH2uKRp0uVDZ6/VkAZQLiozytE2W/G4DBXOi1bv5ELwJ2KaX\nkH7HeoGbvuLmu6KwH+g2ZlI0PQGZGqlNQ9bfWQ/eQx83JyteRw1bc+PtdlvagXXQgW5m37VpdGb7\n2/qlrruNKrqvjb87EgbY39dADuxRGOVyWb6bXKZX6lM9IMyObPKHflqa1yTAcuPxuLj50V+Y7oIM\nrGEwESkYXS9NJ+zs7GB7e1uCdRiws7OzI54PzDUyNTUli5VHjhyB4zgykWj+FoBQPvp52Haa86Ym\nybwp5FUZAm97P7a2MgFDl8/NMcxzHMeRxWZqz5x8TOrGVjZFU16cHLhJB9dcTLA237OmUbTGbJ5r\nc0vkZ50CIRqNinZerVZlkVZTXLxG39dGi9jq63XMNtF4Tcb7lSCg+l6cFBAnUgAADtVJREFUMExF\n4XuaWgH2OqAGci9t29bJvUxHU8MK0sC8RA+U4XAoPt7aJ7zT6SCRSEiCKtd10Wq10Gw2EYlEZAci\nppCl1wi9Smq1moShR6NRyVMei8Wwvb2Na9euodFooFwuC7VD3lnXk4vDNiB3HEcmHXrG0MuF+4JS\nu9cg6KUNm6Cmt23jQquedLgrEV0uTRpE38fcAs6mbfK90dOn3++LVhwGDG0Wn0lRmCBslsln4LVc\nGyCFUq/XJfpTD3pOQrr9TOvBJl5gYU7Wusz7crBim/DDTGT3/J6d9+W+3Jf7cl/85X2tkeuZjRou\nj/tpFDYO1KRXTK3LvM6mkXvRCHRF405GvV4PjUZD+Gz6D5Me0C6CDGABdrVHBt6Uy2WhGRi23+/3\nkcvlhItPJpPodrtYX19Hq9VCrVYb05x1bhFNSen6a/OfVIRe6CN3zR15TJppEo1c/8b78DP9qjW1\nEIaDtmnjZh4T13UlE6IZeWrTpPVnTZ2YmRvN59blmrQfA88ikQhSqZS0R71eH9uFSltJ5vP7adBe\nlAqjX80EXjYK7L7cHfme58h1AzDdLI/b+Ccbx2kz/zWI+00GwPhA9rqnBmoO8uFwd2NgRi7GYjHx\nF6cXy2AwQK1Wk1wrzMQ4GAxkcOdyOWSzWVmA7Pf7KJVKqNfrKBaLmJ+fBwDh0gkgNn9m1709Y6GO\n0uREQs8K+tcz8lVz+Ta+1VyPoGjTnrSInmT4XMw1zvJ4Lb+bFIb5vvQ1mloikO/s7Ny2UOr37im6\nD7Auug/a+hcXm7XXECf8TCYjWTGZ6IuTp26fMCl3g3hyPYHyWfS4uO+1cm/IPbGxhF8otJ8EnTcY\nDMYCbfSONXojCD3YvbhS87jWSLRHhLkQpDu6Pk8PFm6ETE17NBoJ+HW7XQwGA5w5cwaDwQAvvfTS\n/9/e1cTGlRThr2xn7JlJxoSJ4rUdstkkh4UEyXCIIi0ciSIOBHHKDZC48SdxYHfFIVc4cODChR9p\nQUJ74LC7N3YRyoHD7kZahwQwm1hRiOPfsWPPjyeTcTzN4b1q15S7542dkOcn9SdZftPzpl9VV3d1\nVXW9bvtKfq1WQ6VSwalTpwDA5pRXKhWcO3cOMzMzePjwIU6fPo319XWUSiVsbm7aDbUqlQqq1SrG\nxsZQq9XsAcD88pSM0bJnIK1E5k/GpFkBsfLje/iYuSTZSUXO1iCw8yKSfBeAvRG+X24DoCdnLTuX\nrLU1zIdu8B41m5ubdpM0+cq/7Cc8Meucea5XLkLKyUtvtyvbE0DXzo28DlEsFm3KKK+PMF3c9r42\nBvyHc/BEwOsoDBmv1y909ZJpEpJ+55KRix/9GynfJDp93vJe0GvtRHtvva715K7HnAsHQpFry7ff\nhZRE5uL0OXa5ZS61PmOTn9uPkGWZnARcrnY/bigPfH7JhcNARNGLO41GAxsbG/asSU4pfPLkid3B\nEAAmJibsMXCHDh3CxYsXsbKygmKxiHv37uHkyZMol8s2e6XdbqPZbNrTf3jRkGmVVq88cUe2k1QA\n8mAI+V9a7LJtZJvIkANnusgQDVua7XbbGzZhOcv65fOkJ+VS5Aw56NnKzeVydrGRlaakQU/irmwV\n2d9kW+q0V6ZB0ybDJkxXoVCw2Su8FS8rdJ9h5OJZhr2k7OUEpReQXSEwjX5eukuCy4Dq5fW4POAk\nOvuZhFzptxK99JGvr2no9uY+lNSOqSpybZXsdRZMUuQyXtrpRIdAjI+P287OgpGhAl9juwTBlo9W\nKsCOgtPKy8Unx6U5xa1er8OYnZN0BgYGsLKyAmOMPTCi1WqhXq/jzJkzKBaLAKJdFOv1OmZnZ23o\nhHdIHBsbQ6fTsZZ/qVSy7cCKnTNMpBKVbePyTuRgZ4XEXpZM8wRgMy5kW2jo1FCp4F2Kk2nkCVsO\nBC0r12dZD/MkPQ/Jqx5Q+rOkRz9L9g9O+3SFe7RhI/PgmRap5FmZc+iFZSpf8/aFT7QcdRhK0ybH\nqmucuAyWpDHaD7RidvGlJ0X5fZJ17OLBVdYvL76+1s8z5foG/6af5x7YGHk/s1eS4mcrkHN/V1dX\nMTk5CaIo91paRfKZ2jrS9/jKXQPSNZA0b0wHK1kiQqlUsvVsb29jfX0d+Xwe+XwetVoNnU60GZjc\n2Gltbc1avwMD0UHQ9XodtVrNxqh5rxW2uEZGRuyiKVvdHOeWMVrem0a3v8+rkmEKjl9zmpwrxMTX\n+oQfqdg7nU6Xq687fKfT8U4Wkj5pOEj5sRLgnSulUtAKWvLhglY+2gPQe6nofiLXJIDuvYP4jz05\nnsj5Hg6rsPfig6yfaXD9+XjjNnfxLOt7UXBZ3/0aiEkTv663Vx17MSKeF1JV5K1Wy4YFXK5yEpIa\ngq00djPX1tasgmBL2WVxyPqTlLzcpnZhYQGTk5NdSkiGVnwWOceVOfuCrSq2+Le3t22snOssFAoA\n0LVj4OPHj5HL5dBqtVAsFtHpdFAulzE4GB2mfOzYMYyOjtpsB95bZWhoCEtLS/bYNz5sotFo2Nf/\n9aTkUuAuuelB38siN8bs2ieeFaZU6rJuvQiqFxYleDsCaa0C2FWHVmLyzVFXH9H88ISk+7O2gOXv\ndZuyN6k9BwC2766urqJcLluPhMMsT58+tW8K+/qcpkMu7mpaNR2+dSNdN7ddv2i3210TtQsuJcjX\n7EXqdzukgeCjU9fl8r6TeNE6gre10EZgL8vcFVph3nohVUWu3xbcqzuRpMi54diy5Bg5W6SuFzZc\ndbusdulusYJaWFjA+Ph4otWp3f9ms2l39GMlzp2AhXn48GH78gfzIy0xxtbWlj1e7ciRIyiXy1he\nXsbS0hLm5+eRz+cxNTWF7e1tzM7O2uPeRkejk+95IQ+IFN/x48dtvZpu3eldyklnf3C7cDzbNej4\ns7SGtXykHHT2kFbwTEe73baHbUi5aovbFQuV9WuFr+nV/VfWLwcmTzw6LVP+rpeVW61WbdYRL35y\nmiJ7WDrLRPPNbSF5kvdoj0AbN0mW614scpl15EMva9Y3AbmU5348hb2GVjjtVpb52kb3H3lPpkMr\nz8Pt4DADK+6NjQ00m037xqHcoGm/4IbnAaPj8nIA6TJZzq/fE0WHPLDCXllZQb1eR6lUQqvVsif8\ncDqlPEWGs1/40OR8Pm/3Nz979iwePXqE8+fP4+rVqygUCrh+/Tqmp6exubmJra0t1Go1NJtN5PP5\nLksY2LEyNd/6T/MnrWlp0foUYa/FNAnXgJaKhgcA0ywzl/TEo70FfjOV69MZSXJdRE5ELgXHtEgL\ni4is5ekyIqSClfwyT7wwLbey5TAZt/PAwIBNV3XxIPnne1yTKG9CJu+Rbe6yFH3P+H9C86Lp0SEg\nn35xlXNZUqqlXj/R/aOXNe7CXu5NVZGz9cANqwdlEpLu4W1NOWZYrVaxuLiI9fX1Xa6vRi+LXH7X\nbDZtqKZWq2Fubq5LiFphs1KT5bzACUS73PE5nMYY1Ot1VKtVjI6OYmRkBMPDw8jlcvbgBo6VMm3z\n8/OYmJjA3Nwc7t+/j3w+bw8lKJfLGB4exp07dzA8PIyhoSFMTEzgwYMHAGBzvlnJ8O6I/F2z2bTP\nYSUpJ0StuF2KnCc7eZoSt7cxZpd1KvuGjkm75KblKrNu2u223Y+eFbVrcUx+Znrlc31Kw6WUZf0u\nq1B/77M4WQnwdr2Dg4NoNBpYXFzsWpje2tpCo9Ho2qpAGhB6EpV9Uz5XTsxczv1c1uXK/Nov5H7s\nLui2kW0kDw5xwdf+um59LSETF3zPkOA+77vPRY88QYwnfhku8j77eVi++wERpfPggICAgIzCGOOc\nKVNT5AEBAQEBzwdh06yAgICAjCMo8oCAgICMIxVFTkSXieg/RHSHiF5Pg4ZnBRHdJ6J/ENE0EX0c\nlx0loveJ6FMi+gsRjaZNpw9E9DsiWiaiW6LMSz8RvUlEd4lohogupUO1Hx5+rhHRQyL6JP67LL47\n6PycIKK/EdG/iOg2Ef0oLs+kjBz8/DAuz6SMiGiYiD6Kx/9tIroWl6cjH5lS9CL+EE0eswBeBnAI\nwE0Ar75oOp4DH/cAHFVlvwDw0/j6dQA/T5vOHvR/BcAUgFtJ9AP4AoBpRFlOp2L5Udo89MHPNQA/\ncdz7+Qzw8xKAqfj6MIBPAbyaVRn14CfLMirE/wcBfAjgQlryScMivwDgrjHmv8aYLQBvA7iSAh3P\nCsJuj+YKgLfi67cAfPOFUrQHGGP+DmBdFfvo/waAt40xT40x9wHcRSTHAwMPP0AkJ40rOPj8LBlj\nbsbXDQAzAE4gozLy8DMZf51VGTXjy2FECtogJfmkocgnAcyJzw+xI9AswQD4gIhuENH34rIxY8wy\nEHVcAMdTo25/OO6hX8tsHtmR2Q+I6CYR/Va4uZnih4hOIfI2PoS/j2WGJ8HPR3FRJmVERANENA1g\nCcAHxpgbSEk+YbFz/3jNGPNlAF8H8H0i+ioi5S6R9dzOrNP/awCnjTFTiAbbL1OmZ88gosMA/gzg\nx7Elm+k+5uAnszIyxnSMMV9C5CldIKJzSEk+aSjyeQAnxecTcVmmYIxZjP9XALyDyE1aJqIxACCi\nlwCspEfhvuCjfx7A58R9mZCZMaZi4gAlgN9gx5XNBD9ENIRI6f3RGPNuXJxZGbn4ybqMAMAYUwNw\nHcBlpCSfNBT5DQBniehlIsoBuArgvRTo2DeIqBBbFiCiIoBLAG4j4uM78W3fBvCus4KDA0J3fNJH\n/3sArhJRjoheAXAWwMcvisg9oIufeCAxvgXgn/F1Vvj5PYB/G2N+JcqyLKNd/GRVRkR0jMNARJQH\n8DVEcf905JPSau9lRKvWdwG8kQYNz0j/K4iybaYRKfA34vLPAvhrzNv7AD6TNq09ePgTgAUATwA8\nAPBdAEd99AN4E9FK+wyAS2nT3yc/fwBwK5bVO4jil1nh5zUA26KffRKPG28fO8g89eAnkzIC8MWY\nh5sx/T+Ly1ORT3hFPyAgICDjCIudAQEBARlHUOQBAQEBGUdQ5AEBAQEZR1DkAQEBARlHUOQBAQEB\nGUdQ5AEBAQEZR1DkAQEBARlHUOQBAQEBGcf/AMwW6/wETRNtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c9fbfac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[139, 112, 51, 36]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ib = img_with_bbox(img, gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ib2 = img_with_bbox(img, pre_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2ca32203c8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pn/4pZmZmUCqVcOfOHXz5y19GpVJBs9nEd77zHTQaDQyHQ6ysrDiX3KVSCVtb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WAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2ca6a96be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(ib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
